ES2674318T3 - Biomarkers of lung cancer and their uses - Google Patents

Abstract

A method to diagnose that an individual has or does not cancer, the method comprising: detecting, in a sample of whole blood, plasma or serum of an individual, a biomarker value corresponding to CA6, where said individual is classified as afflicted or not suffering from cancer based on said biomarker value.

Description

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DESCRIPTION

Biomarkers of lung cancer and uses thereof Field of the invention

The present application relates generally to the detection of biomarkers and the diagnosis of cancer in an individual and, more particularly, lung cancer, in an individual.

Background

The following description provides a summary of information relevant to the present application and is not an admission that any information provided or publications referred to in this document are prior art of this disclosure.

More people die of lung cancer than any other type of cancer. This is true for both men and women. Lung cancer represents more deaths than breast cancer, prostate cancer and colon cancer combined. Lung cancer accounted for an estimate of 157,300 deaths, or 28% of all cancer deaths in the United States in 2010. It is estimated that in 2010, lung cancers will be diagnosed at 116,750 men and 105,770 women, and 86,220 men and 71,080 women will die of lung cancers (Jemal, CA Cancer J Clin 2010; 60: 277). Among men in the United States, lung cancer is the second most common cancer among white men, blacks, Asians / Pacific Islanders, American Indians / Alaska Natives and Hispanics. Among women in the United States, lung cancer is the second most common cancer among white, black and American Indian / Alaska Native women, and the third most common cancer among Asian / Pacific Islander and Hispanic women. For people who do not quit smoking, the probability of death from lung cancer is 15% and remains above 5% even for those who quit smoking at the age of 50-59. The annual cost of lung cancer health care in the United States alone is $ 95 billion.

Ninety-one percent of lung cancer caused by smoking is non-small cell lung cancer (NSCLC), which accounts for approximately 85% of all lung cancers. The remaining 15% of all lung cancers are microcytic lung cancers, although mixed cell lung cancers do occur. Because microcytic lung cancer is rare and rapidly lethal, the opportunity for early detection is small.

There are three main types of NSCLC: squamous cell carcinoma, macrocytic carcinoma and adenocarcinoma. Adenocarcinoma is the most common form of lung cancer (30% -65%) and is the lung cancer most frequently found in both smokers and non-smokers. Squamous cell carcinoma accounts for 25-30% of all lung cancers and is usually found in a proximal bronchus. Early stage NSCLC tends to be localized and, if detected early, can often be treated by surgery with a favorable outcome and improved survival. Other treatment options include radiation therapy, drug therapy and a combination of these methods.

NSCLC is staged by the size of the tumor and its presence in other tissues including lymph nodes. In the hidden stage, cancer cells can be found in sputum samples or lavage samples and no tumor is detectable in the lungs. In stage 0, only the inner lining of the lungs shows cancer cells and the tumor has not grown through the lining. In stage IA, cancer is considered locally invasive and has grown deep in lung tissue but the tumor is less than 3 cm wide. In this stage, the tumor is not found in the main bronchus or lymph nodes. In stage IB, the tumor is more than 3 cm wide or has grown in the bronchus or pleura, but has not grown in the lymph nodes. In stage IIA, the tumor is less than 7 cm wide and may have grown in the lymph nodes. In stage IIB, the tumor has been found in the lymph nodes and is more than 5 cm wide or grows in the bronchus or pleura; or the cancer is not in the lymph nodes but is found in the chest wall, the diaphragm, the pleura, the bronchus or the tissue surrounding the heart, or separate tumor nodules are present in the same lobe of the lung. In stage IIIA, cancer cells are found in the lymph nodes near the lung and bronchial tubes and in those between the lungs but on the side of the chest where the tumor is located. Stage IIIB, cancer cells are located on the opposite side of the chest to the tumor or in the neck. Other organs near the lungs may also have cancer cells and multiple tumors can be found in a lobe of the lungs. In stage IV, tumors are found in more than one lobe of the same lung or both lungs and cancer cells are found in other parts of the body.

Current diagnostic methods for lung cancers include testing sputum with respect to cancer cells, chest x-ray, fiber optic evaluation and airway biopsy and low dose spiral computed tomography (CT). Sputum cytology has a very low sensitivity. Chest radiography is also relatively insensitive, requiring lesions to be more than 1 cm in size to be visible. Bronchoscopy requires that the tumor be visible within the airways accessible to the

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bronchoscope. The most widely recognized diagnostic method is low-dose thoracic CT, but, in common with radiography, the use of CT involves ionizing radiation, which in itself can cause cancer. CT also has significant limitations: the examinations require a high level of technical ability to interpret them and many of the anomalies observed are not in fact lung cancer and substantial health care costs are incurred in the follow-up of CT findings. The most common incidental finding is a benign pulmonary nodule.

Pulmonary nodules are relatively rounded lesions, or areas of abnormal tissue, located in the lung and their size may vary. Pulmonary nodules can be benign or cancerous, but most are benign. If a nodule is less than 4 mm the prevalence is only 1.5%, if it is 4-8 mm the prevalence is approximately 6%, and if it is more than 20 mm the incidence is approximately 20%. For small and medium sized nodules, the patient is recommended to undergo repeated exploration over a period of three months to a year. For many large nodules, the patient receives a biopsy (which is invasive and can lead to complications) even though most of these are benign.

Therefore, diagnostic methods that can replace or complement CT are necessary to reduce the number of surgical procedures performed and minimize the risk of surgical complications. In addition, even when pulmonary nodules are absent or unknown, methods are needed to detect lung cancer in its early stages to improve patient outcomes. Only 16% of lung cancer cases are diagnosed as early stage, localized cancer, in which the 5-year survival rate is 46%, compared with 84% of those diagnosed in late stage, in which the 5-year survival rate is only 13%. This demonstrates that relying on symptoms for diagnosis is not useful because many of them are common with other lung diseases and often occur only in the late stages of lung cancer. These symptoms include a persistent cough, blood in the sputum, chest pain and bronchitis or recurrent pneumonia.

When there are early diagnostic methods in cancer, the benefits are generally accepted by the medical community. Cancers that have widely used screening protocols have the highest 5-year survival rates, such as breast cancer (88%) and colon cancer (65%) compared to 16% for lung cancer. However, up to 88% of lung cancer patients survive ten years or more if the cancer is diagnosed in Stage I by examination. This demonstrates the clear need for diagnostic methods that can reliably detect early stage NSCLC.

The selection of biomarkers for a specific pathology first involves the identification of markers that have a measurable and statistically significant difference in a diseased population compared to a control population for a specific medical application. Biomarkers can include secreted or detached molecules that are analogous to the development or progression of the disease and easily diffuse into the bloodstream from lung tissue or from distant tissues in response to an injury. They can also include proteins made by cells in response to the tumor. The biomarker or set of identified biomarkers are generally clinically validated or have been shown to be a reliable indicator for the intended original use for which they were selected. Biomarkers can include small molecules, metabolites, peptides, proteins and nucleic acids. Some of the key problems that affect the identification of biomarkers include the over-adjustment of the available data and bias in the data.

Various methods have been used in an attempt to identify biomarkers and diagnose the disease. For protein-based markers, these include two-dimensional electrophoresis, mass spectrometry and immunoassay methods. For nucleic acid markers, these include mRNA expression profiles, microRNA profiles, FISH, serial gene expression analysis (SAGE) and large-scale gene expression matrices.

The utility of two-dimensional electrophoresis is limited by low detection sensitivity; problems with protein solubility, loading and hydrophobia; gel reproducibility; and the possibility that a single point represents multiple proteins. For mass spectrometry, depending on the format used, the limitations depend on the processing and separation of samples, sensitivity to low abundance proteins, signal considerations with respect to noise, and the inability to immediately identify the detected protein. Limitations in immunoassay approaches to biomarker discovery focus on the inability of multiple antibody-based assays to measure a large number of analytes. One could simply print a matrix of high quality antibodies and, without interleaving, measure the analytes bound to those antibodies. (This would be the formal equivalent to the use of a complete genome of nucleic acid sequences to hybridize all DNA or RNA sequences in an organism or a cell. The hybridization experiment works because hybridization can be a rigorous identity test. Even very good antibodies are not rigorous enough for the selection of their binding partners to act in the context of blood or even cell extracts because the protein set in those matrices has extremely different abundances.) Therefore, it should be used A different approach with immunoassay-based approaches to biomarker discovery would require the use of multiple ELISA assays (i.e., sandwich type) to obtain sufficient rigor to measure many analytes simultaneously to decide which analytes are in fact biomarkers. Sandwich type immunoassays cannot be scaled to high

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content, and therefore the discovery of biomarkers using rigorous sandwich immunoassays is not possible using any conventional matrix format. Finally, antibody reagents are subject to substantial variability between batches and reagent instability. The present platform for protein biomarker discovery overcomes this problem.

Many of these methods are based on or require some type of fractionation of samples before analysis. Therefore, the sample preparation required to conduct a sufficiently powerful study designed to identify / discover statistically relevant biomarkers in a series of well-defined sample populations is extremely difficult, expensive and time-consuming. During fractionation, a wide range of molecules can be introduced into the various samples. For example, a potential marker could be unstable for the process, the concentration of the marker could be changed, inappropriate aggregation or disintegration could occur, and involuntary sample contamination could occur and thus hide the anticipated subtle changes in early disease.

It has been widely accepted that the methods of discovery and detection of biomarkers using these technologies have serious limitations for the identification of diagnostic biomarkers. These limitations include an inability to detect biomarkers of low abundance, an inability to uniformly cover the complete dynamic range of the proteome, the irreproducibility in the processing and fractionation of samples and the general irreproducibility and lack of robustness of the method. In addition, these studies have introduced biases in the data and have not adequately addressed the complexity of the sample populations, including appropriate controls, with respect to the random distribution and selection required to identify and validate biomarkers in a target diseased population.

Although efforts have been made to discover new and effective biomarkers for several decades, these efforts have mostly been unsuccessful. Biomarkers have been identified for various diseases normally in academic laboratories, usually by accidental discovery while conducting basic research on some disease process. Based on the discovery and with small amounts of clinical data, articles suggesting the identification of a new biomarker have been published. Most of these proposed biomarkers, however, have not been confirmed as real or useful biomarkers, mainly because the small number of clinical samples tested provides only a weak statistical test that an effective biomarker has indeed been discovered. That is, the initial identification was not rigorous with respect to the basic elements of statistics. In each year from 1994 to 2003, a search of the scientific literature shows that thousands of references to biomarkers were published. During that same period of time, however, the FDA approved a maximum of three new protein biomarkers per year for diagnostic use and in several years no new protein biomarker was approved.

Based on the history of attempts to discover failed biomarkers, mathematical theories have been proposed that further promote the general understanding that disease biomarkers are rare and difficult to find. Biomarker research based on two-dimensional gels or mass spectrometry supports these ideas. Very few useful biomarkers have been identified by these approaches. However, it is usually overlooked that two-dimensional gel and mass spectrometry measure proteins that are present in the blood at concentrations of approximately 1 nM and higher, and that this set of proteins may be the least likely to change with the illness. Apart from this biomarker discovery platform, there are no biomarker discovery platforms that are capable of accurately measuring protein expression levels at much lower concentrations.

Much is known about biochemical pathways for complex human biology. Many biochemical pathways culminate in or are initiated by secreted proteins that act locally in the pathology, for example growth factors are secreted to stimulate the replication of other cells in the pathology, and other factors are secreted to protect the immune system and so on. Although many of these secreted proteins act in a paracrine manner, some act distally in the body. One skilled in the art with a basic understanding of the biochemical pathways would understand that there must be many specific blood pathology proteins at concentrations below (even well below) the limits of detection of two-dimensional gels and mass spectrometry. What must precede the identification of this relatively abundant number of disease biomarkers is a proteomic platform that can analyze proteins at concentrations below those detectable by two-dimensional gels or mass spectrometry.

US2003 / 0215895 describes the development of the murine monoclonal antibody DS6. This antibody reacts immunohistochemically with an antigen, CA6, which is expressed in human serous ovarian carcinomas but is not expressed in superficial epithelium or normal ovarian mesothelium.

Consequently, there is a need for biomarkers, methods, devices, reagents, systems and kits that allow (a) to explore high-risk smokers with respect to lung cancer (b) differentiation of benign pulmonary nodules from malignant pulmonary nodules; (c) the detection of lung cancer biomarkers; Y

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(d) the diagnosis of lung cancer.

Summary

The present invention is defined in its broadest sense in the appended claims.

The present disclosure includes biomarkers, methods, reagents, devices, systems and kits for the detection and diagnosis of cancer and, more particularly, NSCLC. The biomarkers of the present disclosure were identified using a multiple aptamer-based assay that is described in detail in Example 1. Using the aptamer-based biomarker identification method described herein, the present disclosure describes a surprisingly large number of NSCLC biomarkers that are useful for the detection and diagnosis of NSCLC as well as a large number of cancer biomarkers that are useful for the detection and diagnosis of cancer more generally. To identify these biomarkers, more than 1000 proteins were measured from hundreds of individual samples, some of which were at concentrations in the low femtomolar range. This is approximately four orders of magnitude smaller than the biomarker discovery experiments performed with two-dimensional gels and / or mass spectrometry.

Although certain described NSCLC biomarkers are only useful for detecting and diagnosing NSCLC, methods for grouping multiple subsets of the NSCLC biomarkers that are useful as a biomarker panel are described herein. Once an individual biomarker or a subset of biomarkers has been identified, the detection or diagnosis of NSCLC can be achieved in an individual using any platform or assay format that is capable of measuring differences in the levels of the biomarker or biomarkers selected in A biological sample

However, only using the aptamer-based biomarker identification method described herein, where more than 1000 values of potential biomarkers separated from a large number of individuals who had previously been diagnosed who had or were not previously diagnosed were individually explored. CPNM was possible to identify the CPNM biomarkers disclosed in this document. This approach to discovery is in clear contrast to the discovery of biomarkers of conditioned media or lysed cells since it consults a more pertinent system for the patient that does not require translation into human pathology.

Therefore, in one aspect of the present disclosure one or more biomarkers are provided for use alone or in various combinations to diagnose NSCLC or allow differential diagnosis of NSCLC of benign conditions such as those found in individuals with indeterminate pulmonary nodules identified with a CT scan or other image capture method, high-risk smoking screening for NSCLC and diagnosis of an individual with NSCLC. Exemplary embodiments include the biomarkers provided in Table 1, which, as indicated above, were identified using a multiple aptamer-based assay, as generally described in Example 1 and more specifically in Example 2 and 5. The markers provided in Table 1 are useful in the diagnosis of NSCLC in a high-risk population and to distinguish benign lung diseases in individuals who undetermined pulmonary nodules of NSCLC.

Although certain described NSCLC biomarkers are useful alone for detecting and diagnosing NSCLC, methods for grouping multiple subsets of the NSCLC biomarkers that are each useful as a panel of two or more biomarkers are also described herein. Therefore, various embodiments of the present application provide combinations comprising N biomarkers, wherein N is at least two biomarkers. In other embodiments, N is selected to be any number of 2-59 biomarkers.

In other aspects, N is selected to be any number of 2-5, 2-10, 2-15, 2-20, 2-25, 2-30, 2-35, 240, 2-45, 2- 50, 2-55 or 2-59. In other embodiments, N is selected to be any number of 3-5, 3-10, 315, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3-50 , 3-55 or 3-59. In other embodiments, N is selected to be any number of 4-5, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4 -50, 4-55 or 4-59. In other embodiments, N is selected to be any number of 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40, 5-45, 5-50, 5 -55 or 5-59. In other embodiments, N is selected to be any number of 6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45,

6- 50, 6-55 or 6-59. In other embodiments, N is selected to be any number of 7-10, 7-15, 7-20, 7-25,

7- 30, 7-35, 7-40, 7-45, 7-50, 7-55 or 7-59. In other embodiments, N is selected to be any number of

8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 8-50, 8-55 or 8-59. In other embodiments, N is selected to be any number of 9-10, 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9 -55 or 9-59. In other embodiments, N is selected to be any number of 10-15, 10-20, 10-25, 10-30, 10-35, 10-40, 10-45, 10-50, 10-55 or 1059 It will be appreciated that N can be selected to cover similar, but higher order intervals.

In another aspect, a method for diagnosing NSCLC in an individual, including the method of detecting, in a biological sample of an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers provided in Table 1 is provided. , where the individual

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classifies as suffering from NSCLC based on at least one biomarker value.

In another aspect, a method for diagnosing NSCLC in an individual is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to at least one of N biomarkers selected from the group of biomarkers set forth in the Table 1, where the probability that the individual has NSCLC is determined based on the biomarker values.

In another aspect, a method for diagnosing NSCLC in an individual is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to at least one of N biomarkers selected from the group of biomarkers set forth in the Table 1, where the individual is classified as suffering from NSCLC based on biomarker values and where N = 2-10.

In another aspect, a method for diagnosing NSCLC in an individual is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to at least one of N biomarkers selected from the group of biomarkers set forth in the Table 1, where the probability that the individual has NSCLC is determined based on the biomarker values and where N = 2-10.

In another aspect, a method is provided to diagnose that an individual does not have NSCLC, including the method of detecting, in a biological sample of an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers set forth in the Table 1, where the individual is classified as not suffering from NSCLC based on at least one biomarker value.

In another aspect, a method is provided to diagnose that an individual does not have NSCLC, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to one of at least N biomarkers selected from the exposed biomarker group. in Table 1, where the individual is classified as not suffering from NSCLC based on biomarker values and where N = 2-10.

In another aspect, a method for diagnosing NSCLC is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a panel of N biomarkers, where the biomarkers are selected from the group. of biomarkers shown in Table 1, where a classification of biomarker values indicates that the individual has NSCLC and where N = 3-10.

In another aspect, a method for diagnosing NSCLC is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a biomarker panel selected from the group of panels set forth in tables 2 -11, where a classification of biomarker values indicates that the individual has NSCLC.

In another aspect, a method is provided to diagnose an absence of NSCLC, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a panel of N biomarkers, where the biomarkers are selected from the group of biomarkers set forth in Table 1, where a classification of biomarker values indicates an absence of NSCLC in the individual and where N = 3-10.

In another aspect, a method is provided to diagnose an absence of NSCLC, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a panel of N biomarkers, where the biomarkers are selected from the group of biomarkers set forth in Table 1, where a classification of biomarker values indicates an absence of NSCLC in the individual and where N = 3-10.

In another aspect, a method is provided to diagnose an absence of NSCLC, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a biomarker panel selected from the panel group provided in Tables 2-11, where a classification of biomarker values indicates an absence of NSCLC in the individual.

In another aspect, a method for diagnosing NSCLC in an individual is provided, including the method of detecting, in a biological sample of an individual, biomarker values corresponding to one of at least N biomarkers selected from the biomarker group set forth in Table 1. , where the individual is classified as suffering from NSCLC based on a classification score that deviates from a predetermined threshold and where N = 2-10.

In another aspect, a method is provided to diagnose an absence of NSCLC in an individual, including the method of detecting, in a biological sample of an individual, biomarker values corresponding to one of at least N biomarkers selected from the biomarker group exposed in Table 1, wherein said individual is classified as not suffering from NSCLC based on a classification score that deviates from a predetermined threshold and where N = 2-10.

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In another aspect, a computer-implemented method is provided to indicate a probability of NSCLC. The method comprises: retrieving biomarker information for an individual on a computer, where the biomarker information comprises biomarker values that each correspond to one of at least N biomarkers, where N is as defined above, selected from the group. of biomarkers set forth in Table 1; make a classification of each of the biomarker values with the computer; and indicate a probability that the individual has NSCLC based on a plurality of classifications.

In another aspect, a computer-implemented method is provided to classify an individual as suffering or not suffering from NSCLC. The method comprises: retrieving biomarker information for an individual on a computer, wherein the biomarker information comprises biomarker values each corresponding to one of at least N biomarkers selected from the biomarker group provided in Table 1; make a classification of each of the biomarker values with the computer; and indicate whether the individual has NSCLC based on a plurality of classifications.

In another aspect, a computer program product is provided to indicate a probability of NSCLC. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise biomarker values that each correspond to one of at least N biomarkers, wherein N is as defined above, in the biological sample selected from the group of biomarkers set forth in Table 1; and code that executes a classification method that indicates a probability that the individual has NSCLC based on biomarker values.

In another aspect, a computer program product is provided to indicate a CPNM status of an individual. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise biomarker values that each correspond to one of at least N biomarkers in the biological sample selected from the biomarker group provided in Table 1; and code that executes a classification method that indicates an individual's NSCLC status based on biomarker values.

In another aspect, a computer-implemented method is provided to indicate a probability of NSCLC. The method comprises recovering in a computer biomarker information for an individual, wherein the biomarker information comprises a biomarker value corresponding to a biomarker selected from the group of biomarkers set forth in Table 1; perform a classification of the biomarker value with the computer; and indicate a probability that the individual has NSCLC based on the classification.

In another aspect, a computer-implemented method is provided to classify an individual as suffering or not suffering from NSCLC. The method comprises retrieving biomarker information for an individual from a computer, wherein the biomarker information comprises a biomarker value that corresponds to a biomarker selected from the biomarker group provided in Table 1; perform a classification of the biomarker value with the computer; and indicate if the individual has NSCLC based on the classification.

In yet another aspect, a computer program product is provided to indicate a probability of NSCLC. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers set forth in Table 1; and code that executes a classification method that indicates a probability that the individual has NSCLC based on the biomarker value.

In yet another aspect, a computer program product is provided to indicate a CPNM status of an individual. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers provided in Table 1; and code that executes a classification method that indicates an individual's NSCLC status based on the biomarker value.

Although certain biomarkers described are also useful alone to detect and diagnose general cancer, methods for grouping multiple subsets of biomarkers that are useful as a panel of biomarkers for detecting and diagnosing cancer in general are described herein. Once an individual biomarker or a subset of biomarkers has been identified, the detection or diagnosis of cancer in an individual can be achieved using any platform or assay format that is capable of measuring differences in the levels of the biomarker or biomarkers selected in A biological sample

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However, only using the aptamer-based biomarker identification method described herein, where more than 1000 values of potential biomarkers separated from a large number of individuals who had previously been diagnosed who had or were not previously diagnosed were individually explored. Cancer was possible to identify the cancer biomarkers disclosed in this document. This approach to discovery is in clear contrast to the discovery of biomarkers of conditioned media or lysed cells since it consults a more pertinent system for the patient that does not require translation into human pathology.

Therefore, in one aspect of the present application, one or more biomarkers are provided for use alone or in various combinations to diagnose cancer. Exemplary embodiments include the biomarkers provided in Table 19, which were identified using a multiple aptamer-based assay, as generally described in Example 1 and more specifically in Example 6. The markers provided in Table 19 They are useful for distinguishing individuals who have cancer from those who do not have cancer.

Although certain cancer biomarkers described are useful alone for detecting and diagnosing cancer, methods for grouping multiple subsets of cancer biomarkers that are each useful as a panel of three or more biomarkers are also described herein. Therefore, various embodiments of the present application provide combinations comprising N biomarkers, wherein N is at least three biomarkers. In other embodiments, N is selected to be any number of 3-23 biomarkers.

In yet other embodiments, N is selected to be any number of 2-5, 2-10, 2-15, 2-20 or 2-23. In other embodiments, N is selected to be any number of 3-5, 3-10, 3-15, 3-20 or 3-23. In other embodiments, N is selected to be any number of 4-5, 4-10, 4-15, 4-20 or 4-23. In other embodiments, N is selected to be any number of 5-10, 5-15, 5-20 or 5-23. In other embodiments, N is selected to be any number of 6-10, 6-15, 6-20 or 6-23. In other embodiments, N is selected to be any number of 7-10, 7-15, 7-20 or 7-23. In other embodiments, N is selected to be any number of 8-10, 8-15, 8-20 or 8-23. In other embodiments, N is selected to be any number of 9-10, 9-15, 9-20 or 9-23. In other embodiments, N is selected to be any number of 10-15, 10-20 or 10-23. It will be appreciated that N can be selected to cover similar, but higher order intervals.

In another aspect, there is provided a method for diagnosing cancer in an individual, including the method of detecting, in a biological sample of an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers provided in Table 19. , where the individual is classified as suffering from cancer based on at least one biomarker value.

In another aspect, a method for diagnosing cancer in an individual is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to at least one of N biomarkers selected from the group of biomarkers set forth in the Table 19, where the probability that the individual has cancer is determined based on the biomarker values.

In another aspect, a method for diagnosing cancer in an individual is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to at least one of N biomarkers selected from the group of biomarkers set forth in the Table 19, where the individual is classified as suffering from cancer based on biomarker values and where N = 3-10.

In another aspect, a method for diagnosing cancer in an individual is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to at least one of N biomarkers selected from the group of biomarkers set forth in the Table 19, where the probability that the individual has cancer is determined based on the biomarker values and where N = 3-10.

In another aspect, a method is provided to diagnose that an individual does not have cancer, including the method of detecting, in a biological sample of an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers exposed in the Table 19, where the individual is classified as not suffering from cancer based on at least one biomarker value.

In another aspect, a method is provided to diagnose that an individual does not have cancer, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to one of at least N biomarkers selected from the exposed biomarker group. in Table 19, where the individual is classified as not suffering from cancer based on biomarker values and where N = 3-10.

In another aspect, a method for diagnosing cancer is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a panel of N biomarkers, wherein the biomarkers are selected from the group. of biomarkers shown in Table 19, where a classification of biomarker values indicates that the individual has cancer and where N = 310.

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In another aspect, a method for diagnosing cancer is provided, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a biomarker panel selected from the group of panels set forth in tables 20. -29 where a classification of biomarker values indicates that the individual has cancer.

In another aspect, a method is provided to diagnose an absence of cancer, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a panel of N biomarkers, where the biomarkers are selected from the group of biomarkers set forth in Table 19, where a classification of biomarker values indicates an absence of cancer in the individual and where N = 3-10.

In another aspect, a method is provided to diagnose an absence of cancer, including the method of detecting, in a biological sample of an individual, biomarker values that each correspond to a biomarker in a biomarker panel selected from the panel group provided in Tables 20-29, where a classification of biomarker values indicates an absence of cancer in the individual.

In another aspect, a method for diagnosing cancer in an individual is provided, including the method of detecting, in a biological sample of an individual, biomarker values corresponding to one of at least N biomarkers selected from the biomarker group set forth in Table 19. , where the individual is classified as suffering from cancer based on a classification score that deviates from a predetermined threshold and where N = 3-10.

In another aspect, a method is provided to diagnose an absence of cancer in an individual, including the method of detecting, in a biological sample of an individual, biomarker values corresponding to one of at least N biomarkers selected from the group of biomarkers exposed in Table 19, wherein said individual is classified as not suffering from cancer based on a classification score that deviates from a predetermined threshold and where N = 3-10.

In another aspect, a computer-implemented method is provided to indicate a probability of cancer. The method comprises: retrieving biomarker information for an individual on a computer, where the biomarker information comprises biomarker values that each correspond to one of at least N biomarkers, where N is as defined above, selected from the group. of biomarkers set forth in Table 19; make a classification of each of the biomarker values with the computer; and indicate a probability that the individual has cancer based on a plurality of classifications.

In another aspect, a computer-implemented method is provided to classify an individual as suffering or not suffering from cancer. The method comprises: retrieving biomarker information for an individual from a computer, wherein the biomarker information comprises biomarker values that each correspond to at least one of N biomarkers selected from the biomarker group provided in Table 19; make a classification of each of the biomarker values with the computer; and indicate if the individual has cancer based on a plurality of classifications.

In another aspect, a computer program product is provided to indicate a probability of cancer. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise biomarker values that each correspond to one of at least N biomarkers, wherein N is as defined above, in the biological sample selected from the group of biomarkers set forth in Table 19; and code that executes a classification method that indicates a probability that the individual has cancer based on biomarker values.

In another aspect, a computer program product is provided to indicate a cancer status of an individual. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise biomarker values that each correspond to one of at least N biomarkers in the biological sample selected from the biomarker group provided in Table 19; and code that executes a classification method that indicates an individual's cancer status based on biomarker values.

In another aspect, a computer-implemented method is provided to indicate a probability of cancer. The method comprises recovering in a computer biomarker information for an individual, wherein the biomarker information comprises a biomarker value corresponding to a biomarker selected from the group of biomarkers set forth in Table 19; perform a classification of the biomarker value with the computer; and indicate a probability that the individual has cancer based on the classification.

In another aspect, a computer-implemented method is provided to classify an individual as

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suffering or not suffering from cancer. The method comprises retrieving biomarker information for an individual from a computer, wherein the biomarker information comprises a biomarker value corresponding to a biomarker selected from the biomarker group provided in Table 19; perform a classification of the biomarker value with the computer; and indicate if the individual has cancer based on the classification.

In yet another aspect, a computer program product is provided to indicate a probability of cancer. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers set forth in Table 19; and code that executes a classification method that indicates a probability that the individual has cancer based on the biomarker value.

In yet another aspect, a computer program product is provided to indicate a cancer status of an individual. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers provided in Table 19; and code that executes a classification method that indicates an individual's cancer status based on the biomarker value.

Brief description of the drawings

Figure 1A is a flow chart for an exemplary method for detecting NSCLC in a biological sample.

Figure 1B is a flow chart for an exemplary method for detecting NSCLC in a biological sample using a simple Bayesian classification method.

Figure 2 shows an ROC curve for a single biomarker, MMP7, using a simple Bayesian classifier for an assay that detects NSCLC.

Figure 3 shows ROC curves for biomarker panels of two to ten biomarkers using simple Bayesian classifiers for a test that detects NSCLC.

Figure 4 illustrates the increase in the classification score (ABC) as the number of biomarkers increases from one to ten using simple Bayesian classification for a NSCLC panel.

Figure 5 shows the biomarker distributions measured for MMP7 as a cumulative distribution function (fda) in UFR transformed by log for smokers and combined benign lung nodule controls (solid line) and the NSCLC disease group (dotted line) along with its curve adjustments to an fda (dashed lines) used to train simple Bayesian classifiers.

Figure 6 illustrates an exemplary computer system for use with various computer-implemented methods described herein.

Figure 7 is a flow chart for a method to indicate the probability that an individual has NSCLC according to an embodiment.

Figure 8 is a flow chart for a method to indicate the probability that an individual has NSCLC according to an embodiment.

Figure 9 illustrates an exemplary aptamer assay that can be used to detect one or more NSCLC biomarkers in a biological sample.

Figure 10 shows a frequency histogram for which biomarkers were used to construct classifiers to distinguish between NSCLC and smokers and the benign lung nodules control group of an aggregate set of potential biomarkers.

Figure 11A shows a pair of histograms that summarize all possible scores of simple Bayesian classifiers (ABC) of a single protein using the biomarkers shown in Table 1 (black) and a set of random markers (gray).

Figure 11B shows a pair of histograms that summarize all possible scores of simple Bayesian classifiers (ABC) of two proteins using the biomarkers shown in Table 1 (black) and a set of random markers (gray).

Figure 11C shows a pair of histograms that summarize all possible scores of three-protein simple Bayesian classifiers (ABC) using the biomarkers shown in Table 1 (black) and a set of random markers (gray).

Figure 12 shows the ABC for simple Bayesian classifiers using 2 to 10 markers selected from the full panel and the scores obtained by discarding the best 5, 10 and 15 markers during classifier generation.

Figure 13A shows a set of ROC curves modeled from the data in Table 14 for panels of two to five markers.

Figure 13B shows a set of ROC curves calculated from training data for panels of two to five markers as in Figure 12A.

Figure 14 shows an ROC curve calculated from the clinical biomarker panel described in Example 5.

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Figures 15A and 15B show a performance comparison between ten cancer biomarkers selected by an avid selection procedure described in Example 6 (Table 19) and 1000 sets of ten "non-marker" biomarkers randomly sampled. The average ABC for the ten cancer biomarkers in Table 19 is shown as a vertical dotted line. In Figure 15A, sets of ten "non-markers" were randomly selected that were not selected by the avid procedure described in Example 6. In Figure 15B, the same procedure as 15A was used; however, sampling was restricted to the remaining 49 NSCLC biomarkers in Table 1 that were not selected by the avid procedure described in Example 6.

Figure 16 shows curves of receiver operating characteristics (ROC) for the 3 simple Bayesian classifiers shown in Table 31. For each study, the area under the curve (ABC) is also presented next to the legend.

Detailed description

Reference will now be made in detail to representative aspects of the disclosure. The invention is described by the appended claims.

The person skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in and are within the scope of the practice of the present invention. The present invention is not limited in any way by the methods and materials described.

Unless otherwise defined, the technical and scientific terms used herein have the same meaning as an expert in the field to which the present invention pertains. Although some methods, devices and materials similar or equivalent to those described herein may be used in the practice or test of the invention, the preferred methods, devices and materials are described below.

All publications, published patent documents and patent applications cited in this disclosure are indicative of the level of experience in the technique or techniques to which the application belongs.

As used in the present application, including the appended claims, the singular forms "a", "one / one", and "the" include plural references, unless the content clearly imposes otherwise, and they are used interchangeably with "at least one" and "one or more." Therefore, the reference to "an aptamer" includes mixtures of aptamers, the reference to "a probe" includes mixtures of probes, and the like.

As used herein, the term "approximately" represents an insignificant modification or variation of the numerical value so that the basic function of the element to which the numerical value relates does not change.

As used herein, the terms "comprises", "comprising", "including", "including", "containing", "containing", and any variation thereof, are intended to cover an inclusion not exclusive, so that a process, method, product by process or composition of the subject that comprises, includes or contains an element or list of elements does not include only those elements, but may include other elements not expressly enumerated or inherent to said process, method, product by process or composition of matter.

The present disclosure includes biomarkers, methods, devices, reagents, systems and kits for the detection and diagnosis of NSCLC and more general cancer.

In one aspect, one or more biomarkers are provided for use alone or in various combinations to diagnose NSCLC, allowing the differential diagnosis of NSCLC of non-malignant conditions found in individuals with indeterminate lung nodules identified with a CT scan or other capture method. Imaging, high-risk smoking screening for NSCLC and diagnosis of an individual with NSCLC, supervising the return of NSCLC or addressing other clinical indications. As described in detail below, exemplary embodiments include the biomarkers provided in Table 1, which were identified using a multiple aptamer-based assay that is generally described in Example 1 and more specifically in Example 2.

Table 1 presents the findings obtained from the analysis of hundreds of individual blood samples from NSCLC cases and hundreds of equivalent individual control blood samples from high-risk smokers and benign lung nodules. The benign and smoking lung nodules control group was designed to fit the populations with which a NSCLC diagnostic trial may have the greatest benefit, including asymptomatic individuals and symptomatic individuals. These cases and controls were obtained from multiple clinical sites to mimic the range of real-world conditions in which the trial can be applied. Potential biomarkers were measured in individual samples rather than by grouping disease and control blood; this allowed a better understanding of the individual and group variations in the phenotypes associated with the

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presence and absence of disease (in this case NSCLC). Since more than 1000 protein measurements were made in each sample and several hundred samples from each of the disease and control populations were individually measured, Table 1 resulted from an analysis of an unusually large data set. Measurements were analyzed using the methods described in the section, "Classification of biomarkers and calculation of disease scores" in this document. Table 1 lists the 59 biomarkers that have been found to be useful for distinguishing samples obtained from individuals with NSCLC from "control" samples obtained from smokers and benign lung nodules.

Although certain described NSCLC biomarkers are useful alone for detecting and diagnosing NSCLC, methods for grouping multiple subsets of the NMCP biomarkers are also described herein, in which each cluster or subset selection is useful as a panel of three or more biomarkers, referred to interchangeably herein as "biomarker panel" and one panel. Therefore, various embodiments of the present application provide combinations comprising N biomarkers, wherein N is at least two biomarkers. In other embodiments, N is selected from 2-59 biomarkers.

In yet other embodiments, N is selected to be any number of 2-5, 2-10, 2-15, 2-20, 2-25, 2-30, 235, 2-40, 2-45, 2- 50, 2-55 or 2-59. In other embodiments, N is selected to be any number of 3-5, 310, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3-50 , 3-55 or 3-59. In other embodiments, N is selected to be any number of 4-5, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4 -50, 4-55 or 4-59. In other embodiments, N is selected to be any number of 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40, 5-45, 5-50, 5 -55 or 5-59. In other embodiments, N is selected to be any number of 6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45,

6- 50, 6-55 or 6-59. In other embodiments, N is selected to be any number of 7-10, 7-15, 7-20, 7-25,

7- 30, 7-35, 7-40, 7-45, 7-50, 7-55 or 7-59. In other embodiments, N is selected to be any number of

8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 8-50, 8-55 or 8-59. In other embodiments, N is selected to be any number of 9-10, 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9 -55 or 9-59. In other embodiments, N is selected to be any number of 10-15, 10-20, 10-25, 10-30, 10-35, 10-40, 10-45, 10-50, 10-55 or 1059 It will be appreciated that N can be selected to cover similar, but higher order intervals.

In one embodiment, the number of biomarkers useful for a subset or panel of biomarkers is based on the sensitivity value and specificity for the particular combination of biomarker values. The terms "sensitivity" and "specificity" are used herein with respect to the ability to correctly classify an individual, based on one or more biomarker values detected in their biological sample, such as those with NSCLC or not with NSCLC. . "Sensitivity" indicates the performance of the biomarker or biomarkers with respect to the correct classification of individuals who have NSCLC. "Specificity" indicates the performance of the biomarker or biomarkers with respect to the correct classification of individuals who do not have NSCLC. For example, a specificity of 85% and sensitivity of 90% for a panel of markers used to test a set of control samples and NSCLC samples indicates that 85% of the control samples were correctly classified as control samples by the panel, and 90% of the CPNM samples were correctly classified as CPNM samples by the panel. The desired or preferred minimum value can be determined as described in Example 3. Representative panels are set forth in Tables 4-11, which expose a series of 100 different panels of 3-10 biomarkers, which have the indicated levels of specificity and sensitivity. For each panel. The total number of occurrences of each marker on each of these panels is indicated in Table 12.

In one aspect, NSCLC is detected or diagnosed in an individual by performing a test on a biological sample of the individual and detecting biomarker values that each correspond to at least one of the MMP7, CLIC1 or STX1A biomarkers and at least N additional biomarkers selected from the list of biomarkers in Table 1, in which N is equal to 2, 3, 4, 5, 6, 7, 8 or 9. In a further aspect, NSCLC is detected or diagnosed in an individual by performing a test in a biological sample of the individual and detecting biomarker values that each correspond to MMP7, CLIC1 or STX1A biomarkers and one of at least N additional biomarkers selected from the list of biomarkers in Table 1, in which N is equal to 1, 2 , 3, 4, 5, 6 or 7. In a further aspect, NSCLC is detected or diagnosed in an individual by performing a test on a biological sample of the individual and detecting biomarker values that each correspond to the MMP7 biomarker and one of the minus N additional biomarkers selected from the list of biomarkers in Table 1, in which N is equal to 2, 3, 4, 5, 6, 7, 8 or 9. In a further aspect, NSCLC is detected or diagnosed in a individual performing a test on a biological sample of the individual and detecting biomarker values that each correspond to the CLIC1 biomarker and one of at least N additional biomarkers selected from the list of biomarkers in Table 1, in which N is equal to 2, 3, 4, 5, 6, 7, 8 or 9. In a further aspect, NSCLC is detected or diagnosed in an individual by performing a test on a biological sample of the individual and detecting biomarker values that each correspond to the STX1A biomarker and one of at least N additional biomarkers selected from the list of biomarkers in Table 1, in which N is equal to 2, 3, 4, 5, 6, 7, 8 or 9.

The NSCLC biomarkers identified herein represent a relatively large number of options for subsets or biomarker panels that can be used to effectively detect or diagnose NSCLC. The selection of the desired number of said biomarkers depends on the combination

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specific biomarkers chosen. It is important to remember that biomarker panels for detecting or diagnosing NSCLC can also include biomarkers not found in Table 1 and that the inclusion of additional biomarkers not found in Table 1 can reduce the number of biomarkers in the particular subset or panel that is select from Table 1. The number of biomarkers in Table 1 used in a subset or panel can also be reduced along with the biomarker values to establish sensitivity and specificity values for a given assay.

Another factor that can affect the number of biomarkers can be used in a subset or panel of biomarkers is the procedures used to obtain biological samples from individuals diagnosed with NSCLC. In a carefully controlled sample acquisition environment, the number of biomarkers necessary to achieve the desired sensitivity and specificity values will be less than in a situation where there may be more variation in sample collection, handling and storage. In the development of the biomarker list set forth in Table 1, multiple sample collection sites were used to collect data for classifier training. This provides more robust biomarkers that are less sensitive to variations in sample collection, handling and storage, but may also require that the number of biomarkers in a subset or panel be greater than if training data were all obtained under conditions. very similar.

One aspect of the present application can be described in general in reference to Figures 1A and 1B. A biological sample is obtained from an individual or individuals of interest. The biological sample is then tested to detect the presence of one or more (N) biomarkers of interest and to determine a biomarker value for each of said N biomarkers (indicated in Figure 1B as a marker UFR). Once a biomarker has been detected and a biomarker value has been assigned each marker is scored or classified as described in detail herein. Marker scores are then combined to provide a total diagnostic score, which indicates the probability that the individual from whom the sample was obtained has NSCLC.

As used herein, "lung" may be referred to interchangeably as "pulmonary."

As used herein, "smoker" refers to an individual who has a history of inhaling tobacco smoke.

"Biological sample", "sample" and "test sample" are used interchangeably herein to refer to any material, biological fluid, tissue or cell obtained or otherwise derived from an individual. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, leukocyte layer, plasma and serum), sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washes, cystic fluid, Meningeal fluid, amniotic fluid, glandular fluid, lymphatic fluid, cytological fluid, ascites fluid, pleural fluid, nipple aspirate, bronchial aspirate, bronchial brushing, synovial fluid, joint aspiration, organ secretions, cells, a cellular and liquid extract cerebrospinal This also includes experimentally separated fractions of all of the above. For example, a blood sample can be fractionated in serum, plasma or in fractions that contain particular types of blood cells, such as red blood cells or white blood cells (leukocytes). If desired, a sample may be a combination of samples from an individual, such as a combination of a tissue and liquid sample. The term "biological sample" also includes materials containing homogenized solid material, such as a stool sample, a tissue sample or a tissue biopsy, for example. The term "biological sample" also includes materials obtained from a tissue culture or a cell culture. Any suitable method can be used to obtain a biological sample; Exemplary methods include, for example, phlebotomy, hyssop (for example, oral swab) and a fine needle aspiration biopsy procedure. Exemplary tissues susceptible to fine needle aspiration include lymph node, lung, lung washes, LBA (bronchoalveolar lavage), pleura, thyroid, breast, pancreas and liver. Samples may also be collected, for example, by microdissection (eg, laser capture microdissection (MCL) or laser microdissection (MDL)), bladder wash, smear (eg, a PAP smear) or ductal wash. A "biological sample" obtained or derived from an individual includes any such sample that has been processed in any suitable manner after it is obtained from the individual.

In addition, it should be borne in mind that a biological sample can be obtained by taking biological samples from several individuals and grouping them together or grouping an aliquot of a biological sample from each individual. The pooled sample can be treated as a sample of a single individual and if the presence of cancer is established in the pooled sample, then each individual biological sample can be retested to determine which individual or individuals have NSCLC.

For the purposes of the present specification, it is understood that the expression "data attributed to a biological sample of an individual" means that the data was somehow obtained from, or generated using, the biological sample of the individual. The data may have been reformatted, revised or altered mathematically to some degree after it was generated, such as by conversion of units in a measurement system to units in another measurement system; but, it is understood that the data has been obtained from, or generated using,

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The biological sample.

"Diana", "target molecule" and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample. A "molecule of interest" includes any minor variation of a particular molecule, such as, in the case of a protein, for example, minor variations in the amino acid sequence, formation of disulfide bonds, glycosylation, lipidation, acetylation, phosphorylation or any other manipulation or modification, such as conjugation with a labeling component, that does not substantially alter the identity of the molecule. A "target molecule", "target" or "analyte" is a set of copies of a type or species of multimolecular molecule or structure. "Target molecules", "targets" and "analytes" refer to more than one of said sets of molecules. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, antibodies, autoantibodies, antibody mimetics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues and any fragment or part of any of the above.

As used herein, "polypeptide," "peptide," and "protein" are used interchangeably herein to refer to amino acid polymers of any length. The polymer may be linear or branched, may comprise modified amino acids and may be interrupted by compounds other than amino acids. The terms also encompass an amino acid polymer that has been modified naturally or by intervention; for example, formation of disulfide bonds, glycosylation, lipidation, acetylation, phosphorylation or any other manipulation or modification, such as conjugation with a labeling component. Also included in the definition are, for example, polypeptides containing one or more analogs of an amino acid (including, for example, unnatural amino acids, etc.), as well as other modifications known in the art. The polypeptides can be single stranded or associated chains. Also intact mature proteins and preproteins are included in the definition; peptides or polypeptides derived from a mature protein; fragments of a protein; splice variants; recombinant forms of a protein; protein variants with modifications, deletions or substitutions of amino acids; digestion products; and post-translational modifications, such as glycosylation, acetylation, phosphorylation and the like.

As used herein, "marker" and "biomarker" are used interchangeably to refer to a target molecule that indicates or is a signal of a normal or abnormal process in an individual or of a disease or other condition in an individual. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical or molecular parameter associated with the presence of a specific physiological state or process, either normal or abnormal and, if abnormal, either chronic or acute. Biomarkers are detectable and measurable by various methods including laboratory tests and medical imaging. When a biomarker is a protein, it is also possible to use the expression of the corresponding gene as a related measure of the amount or presence or absence of the corresponding protein biomarker in a biological sample or methylation state of the gene encoding the biomarker or proteins that control the Biomarker expression.

As used herein, "biomarker value", "value", "biomarker level" and "level" are used interchangeably to refer to a measurement that is made using any analytical method to detect the biomarker in a biological sample and indicating the presence, absence, absolute amount or concentration, relative amount or concentration, title, a level, an expression level, a ratio of measured levels, or the like, of, for or corresponding to the biomarker in the biological sample. The exact nature of the "value" or "level" depends on the specific design and components of the particular analytical method used to detect the biomarker.

When a biomarker indicates or is a signal of an abnormal process or a disease or other condition in an individual, the biomarker is generally described as overexpressed or under-expressed compared to a level of expression or value of the biomarker that indicates or is a signal of a normal process or an absence of a disease or other condition in an individual. "Positive regulation", "positively regulated", "overexpression", "overexpressed" and any variation thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that is normally detected in similar biological samples from healthy or normal individuals. Expressions can also refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that can be detected at a different stage of a particular disease.

"Negative regulation", "negatively regulated", "under-expression", "under-expressed" and any variation thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that is normally detected in similar biological samples of healthy or normal individuals.Expressions may also refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that can be detected at a different stage of a particular disease.

In addition, a biomarker that is overexpressed or under-expressed may also be indicated as "differentially expressed" or that it has a "differential level" or "differential value" as compared to a level or value of

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"normal" expression of the biomarker that indicates or is a sign of a normal process or an absence of a disease or other condition in an individual. Therefore, the "differential expression" of a biomarker can also be indicated as a variation with respect to a "normal" expression level of the biomarker.

The expression "differential gene expression" and "differential expression" are used interchangeably to refer to a gene (or its corresponding protein expression product) whose expression is activated to a greater or lesser level in a subject suffering from a specific disease, in relation to with its expression in a normal or control subject. Expressions also include genes (or corresponding protein expression products) whose expression is activated to a greater or lesser level at different stages of the same disease. It is also understood that a differentially expressed gene can be activated or inhibited in nucleic acids or proteins, or it can undergo alternative splicing to result in a different polypeptide product. Such differences can be demonstrated by various changes including mRNA levels, surface expression, secretion or other division of a polypeptide. Differential gene expression may include an expression comparison between two or more genes or their gene products; or a comparison of the expression relationships between two or more genes or their gene products; or even a comparison of two products processed differently from the same gene, which differ between normal subjects and subjects suffering from a disease; or between different stages of the same disease. Differential expression includes both quantitative and qualitative differences in the pattern of temporal or cellular expression in a gene its expression products between, for example, normal and diseased cells or between cells that have experienced different disease events or disease stages. .

As used herein, "individual" refers to a test subject or patient. The individual may be a mammal or one other than a mammal. In various embodiments, the individual is a mammal. A mammalian individual can be a human being or one other than a human being. In various embodiments, the individual is a human being. A healthy or normal individual is an individual in which the disease or condition of interest (including, for example, lung diseases, lung-associated diseases or other lung conditions) is not detectable by conventional diagnostic methods.

«Diagnose», «diagnosing», «diagnosis» and variations thereof refer to the detection, determination or recognition of an individual's state of health or condition based on one or more signs, symptoms, data or other information related to that individual. The health status of an individual can be diagnosed as healthy / normal (that is, a diagnosis of absence of a disease or condition) or diagnosed as sick / abnormal (that is, a diagnosis of the presence, or an assessment of the characteristics, of a disease or condition). The terms "diagnose", "diagnosing", "diagnosis", etc., cover, with respect to a particular disease or condition, the initial detection of the disease; the characterization or classification of the disease; the detection of the progression, remission or recurrence of the disease; and the detection of a disease response after the administration of a treatment or therapy to the individual. The diagnosis of NSCLC includes distinguishing individuals who have cancer from individuals who do not. It also includes distinguishing smokers and benign pulmonary nodules of NSCLC.

«Forecasting», «forecasting», «forecasting» and variations thereof refer to the prediction of a future cycle of a disease or condition in an individual who has the disease or condition (eg, prediction of a patient's survival ), and these terms cover the evaluation of disease response after the administration of a treatment or therapy to the individual.

«Evaluate», «evaluating», «evaluation» and variations thereof cover both «diagnose» and «predict» and also include determinations or predictions about the future evolution of a disease or condition in an individual who does not have the disease as well as determinations or predictions regarding the probability that a disease or condition will reappear in an individual who has apparently been cured of the disease. The term "evaluate" also encompasses evaluating an individual's response to a therapy, such as, for example, predicting whether an individual is likely to respond favorably to a therapeutic agent or is unlikely to respond to a therapeutic agent (or will experience side effects). toxic or other undesirable, for example), select a therapeutic agent for administration to an individual or monitor or determine an individual's response to a therapy that has been administered to the individual. Therefore, "assessing" NSCLC may include, for example, any of the following: forecasting the future evolution of NSCLC in an individual; predict the recurrence of NSCLC in an individual who has apparently been cured of NSCLC; or determine or predict an individual's response to an NSCLC treatment or select an NSCLC treatment to administer to an individual based on a determination of the biomarker values derived from the individual's biological sample.

It can be indicated that any of the following examples "diagnoses" or "evaluates" NSCLC: initially detect the presence or absence of NSCLC; determine a specific stage, type or subtype, or other classification or characteristic of NSCLC; determine whether a nodule or a suspicious lung mass is benign or malignant NSCLC; or detect / monitor the progression of NSCLC (for example, monitor tumor growth or metastatic spread), remission or recurrence.

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As used herein, "additional biomedical information" refers to one or more evaluations of an individual, other than the use of any of the biomarkers described herein, that are associated with cancer risk or, more specifically , NSCLC risk. "Additional biomedical information" includes any of the following: physical descriptors of an individual, physical descriptors of a pulmonary nodule observed by CT image capture, the height and / or weight of an individual, the sex of an individual, ethnicity of an individual, smoking history, occupational history, exposure to known carcinogens (for example, exposure to any asbestos, radon gas, chemicals, smoke fires and air pollution, which may include emissions from stationary or mobile sources such as industrial / factory or car / ship / aircraft emissions), exposure to foreign smoke, family history of NSCLC (or other cancer), the presence of pulmonary nodules, the size of nodules, the location of nodules, the morphology of nodules ( for example, as observed by CT image capture, opaque glass opaque (OVD), solid, non-solid), characteristics of the edge of the n nodule (eg, smooth, lobed, acute and smooth, spiculated, infiltrant) and the like. Smoking history is usually quantified with respect to "years package", which refers to the number of years a person has smoked multiplied by the average number of packages smoked per day. For example, it is indicated that a person who has smoked, on average, a pack of cigarettes a day for 35 years has 35 years of smoking history packages. Additional biomedical information can be obtained from an individual using routine techniques known in this field, such as from the individuals themselves by using a routine patient questionnaire or health history questionnaire, etc., or from a medical practitioner, etc. . Alternatively, additional biomedical information can be obtained from routine image capture techniques, including CT image capture (eg, low dose CT image capture) and radiography. The biomarker level test in combination with an evaluation of any additional biomedical information may, for example, improve the sensitivity, specificity and / or ABC for detecting NSCLC (or other uses related to NSCLC) compared to biomarker assay alone or evaluation. of any particular element of additional biomedical information alone (for example, CT image capture alone).

The term "area under the curve" or "ABC" refers to the area under the curve of a receiver operating characteristic curve (ROC), both well known in the art. ABC measurements are useful for comparing the accuracy of a classifier across the entire data range. Classifiers with an ABC have a greater ability to classify strangers correctly between two interest groups (for example, NSCLC samples and normal or control samples). ROC curves are useful for representing the performance of a particular element (for example, any of the biomarkers described herein and / or any additional biomedical information element) in the distinction between two populations (for example, cases that have CPNM and controls without CPNM). Normally, element data throughout the entire population (for example, cases and controls) are sorted in ascending order based on the value of a single element. Then, for each value for that element, the true positive and false positive rates for the data are calculated. The true positive rate is determined by counting the number of cases above the value for that element and then dividing by the total number of cases. The false positive rate is determined by counting the number of controls above the value for that element and then dividing by the total number of controls. Although this definition refers to scenarios in which an element is elevated in cases compared to controls, this definition also applies to scenarios in which an element is lower in cases compared to controls (in that scenario, samples below would be counted of the value for that item). ROC curves can be generated for a single element as well as for other individual results, for example, a combination of two or more elements can be combined mathematically (for example, added, subtracted, multiplied, etc.) to provide a single sum value, and this single sum value can be represented in a ROC curve. Additionally, any combination of multiple elements, in which the combination derives a single result value, can be represented in a ROC curve. These combinations of elements may comprise an essay. The ROC curve is the representation of the true positive rate (sensitivity) of an assay versus the false positive rate (1-specificity) of the assay.

As used herein, "detect" or "determine" with respect to a biomarker value includes the use of both the instrument required to observe and record a signal corresponding to a biomarker value and the material or materials required to generate that signal. In various embodiments, the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, tunnel effect microscopy , electrochemical detection methods, nuclear magnetic resonance, quantum dots and the like.

"Solid support" refers herein to any substrate that has a surface to which molecules can be attached, directly or indirectly, by covalent or non-covalent bonds. A "solid support" may have various physical formats, which may include, for example, a membrane; a chip (for example, a protein chip); a slide (for example, a glass slide or coverslip); a column; a hollow, solid, semi-solid particle, which contains pores or cavities, such as, for example, a pearl; a gel; a fiber, including a fiber optic material; An array; and a sample receptacle. Exemplary sample receptacles include sample wells, tubes, capillaries, vials and any other vessel, groove or slit capable of containing a sample. A sample receptacle may be contained in a multisample platform, such as a microtiter plate, a slide, a microfluidic device and the like. A stand can

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be composed of a natural or synthetic material, an organic or inorganic material. The composition of the solid support in which the capture reagents are bound generally depends on the method of binding (eg, covalent bonding). Other exemplary receptacles include micro-drops and oil / aqueous emulsions controlled by microfluids or in bulk in which related tests and manipulations can be performed. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glasses, membranes, nylon, natural fibers (such as, for example, silk, wool and cotton), polymers and the like. The material comprising the solid support may include reactive groups such as, for example, carboxy, amino or hydroxyl groups, which are used for binding of capture reagents. Polymeric solid supports may include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber (natural rubber poly) tetrafluoroethylene, (poly) vinylidene fluoride, polycarbonate and polymethylpentene. Suitable solid support particles that can be used include, for example, encoded particles, such as Luminex type encoded particles, magnetic particles and glass particles.

Uses of example biomarkers

In various exemplary embodiments, methods for diagnosing NSCLC in an individual are provided by detecting one or more biomarker values corresponding to one or more biomarkers that are present in the circulation of an individual, such as in serum or plasma, by any of various analytical methods, including any of the analytical methods described herein. These biomarkers, for example, are differentially expressed in individuals with NSCLC compared to individuals without NSCLC. Detection of the differential expression of a biomarker can be used in an individual, for example, to allow early diagnosis of NSCLC, to distinguish between a benign and malignant pulmonary nodule (such as, for example, a nodule observed in a CT scan. (CT)), to monitor the recurrence of NSCLC or for other clinical indications.

Any of the biomarkers described herein can be used in various clinical indications for NSCLC, including any of the following: detection of NSCLC (such as in an individual or high-risk population); characterization of NSCLC (for example, to determine the type, subtype or stage of NSCLC), such as distinguishing between non-small cell lung cancer (NSCLC) and microcytic lung cancer (CPM) and / or between adenocarcinoma and squamous cell carcinoma (or to facilitate histopathology in another way); determine if a pulmonary nodule is a benign nodule or a malignant lung tumor; determine the prognosis of NSCLC; monitor the progression or remission of NSCLC; supervise the reappearance of NSCLC; monitor metastasis; treatment selection; monitor the response to a therapeutic agent or other treatment; stratification of individuals for computed tomography (CT) scanning (for example, to identify the individuals with the highest risk of NSCLC and therefore most likely to benefit from spiral CT scanning, thereby increasing the positive predictive value of CT) ; combine biomarker trials with additional biomedical information, such as smoking history, etc., or with nodule size, morphology, etc. (such as to provide an assay with improved diagnostic performance compared to CT tests or biomarker assays only); facilitate the diagnosis of a pulmonary nodule as malignant or benign; facilitate clinical decision-making once a pulmonary nodule has been observed on CT (for example, asking for repeated CT scans if the nodule is considered to be low risk, such as if a biomarker-based trial is negative, with or without categorization of the nodule size, or considering biopsy if the nodule is interpreted to be medium to high risk, such as if a biomarker-based assay is positive, with or without categorization of the nodule size); and facilitate decisions regarding clinical follow-up (for example, whether to implement repeated CT scans, fine needle biopsy, nodule resection or thoracotomy after observing a non-calcified nodule on CT). The biomarker assay can improve positive predictive value (PPV) with respect to CT scan or chest radiography of high-risk individuals only. In addition to their utilities along with CT scanning, the biomarkers described herein can also be used in conjunction with any other imaging modality used for NSCLC, such as chest x-ray, bronchoscopy or fluorescent bronchoscopy, MRI scan or PET. In addition, the described biomarkers may also be useful to allow some of these uses before detecting indications of NSCLC by capturing images of modalities or other clinical correlates, or before symptoms appear. It also includes distinguishing individuals with undetermined lung nodules identified with a CT scan or other imaging method, high-risk smoking screening for NSCLC and diagnosis of an individual with NSCLC.

As an example of how any of the biomarkers described herein can be used to diagnose NSCLC, the differential expression of one or more of the biomarkers described in an individual who is not known to have NSCLC may indicate that the individual has NSCLC, thus allowing the detection of NSCLC at an early stage of the disease when the treatment is most effective, perhaps before the NSCLC is detected by other means or before symptoms appear. Overexpression of one or more of the biomarkers during the course of NSCLC may be indicative of progression of NSCLC, for example, a tumor of NSCLC grows and / or metastasizes (and therefore indicates a poor prognosis), while a reduction to the extent that one or more of the biomarkers is differentially expressed (i.e. in biomarker trials

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Subsequently, the level of expression in the individual moves toward or near a level of "normal" expression) may be indicative of remission of NSCLC, for example, a tumor of NSCLC is shrinking (and therefore indicates a good prognosis or better). Similarly, an increase in the degree to which one or more of the biomarkers is differentially expressed (ie, in subsequent biomarker trials, the level of expression in the individual moves away from a "normal" level of expression) during the The course of the NSCLC treatment may indicate that the NSCLC is progressing and therefore indicates that the treatment is ineffective, while a reduction in the differential expression of one or more of the biomarkers during the course of the NSCLC treatment may be indicative of the CPNM remission and therefore indicates that the treatment is working successfully. Additionally, an increase or reduction in the differential expression of one or more of the biomarkers after an individual has apparently cured of NSCLC may be indicative of the recurrence of NSCLC. In a situation like this, for example, the individual may restart the therapy (or the modified therapeutic regimen such as to increase the amount and / or frequency of dosing, if the individual has maintained the therapy) at an earlier stage than if the CPNM reappearance was not detected until later. In addition, a level of differential expression of one or more of the biomarkers in an individual may be predictive of the individual's response to a particular therapeutic agent. To monitor the recurrence or progression of NSCLC, changes in biomarker expression levels may indicate the need to repeat image capture (eg, repeat CT scan), such as to determine the activity of NSCLC or to determine The need for changes in treatment.

The detection of any of the biomarkers described herein can be particularly useful after, or in conjunction with, treatment of NSCLC, such as to evaluate the success of the treatment or to monitor remission, recurrence and / or progression (including metastasis). of NSCLC after treatment. The treatment of NSCLC may include, for example, the administration of a therapeutic agent to the individual, the performance of surgery (for example, surgical resection of at least a part of a NSCLC tumor or removal of NSCLC and surrounding tissue), administration of radiotherapy or any other type of NSCLC treatment used in the art, and any combination of these treatments. The treatment of lung cancer may include, for example, the administration of a therapeutic agent to the individual, the performance of surgery (for example, surgical resection of at least a part of a lung tumor), the administration of radiotherapy or any other type of NSCLC treatment used in the art, and any combination of these treatments. For example, siRNA molecules are synthetic double stranded RNA molecules that inhibit gene expression and can act as targeted lung cancer therapeutic products. For example, any of the biomarkers can be detected at least once after treatment or can be detected multiple times after treatment (such as at periodic intervals) or can be detected both before and after treatment. Differential expression levels of any of the biomarkers in an individual over time may be indicative of progression, remission or recurrence of NSCLC, the examples of which include any of the following: an increase or a reduction in the level of expression of the biomarkers after treatment compared to the level of biomarker expression before treatment; an increase or decrease in the level of biomarker expression at a later time point after treatment compared to the level of biomarker expression at a time point earlier after treatment; and a level of differential expression of the biomarker at a single time point after treatment compared to normal levels of the biomarker.

As a specific example, biomarker levels for any of the biomarkers described herein can be determined in pre-surgery and post-surgery serum or plasma samples (for example, 216 weeks after surgery). An increase in the level or levels of biomarker expression in the post-surgery sample compared to the pre-surgery sample may indicate the progression of NSCLC (for example, unsuccessful surgery), while a reduction in the level or levels of expression of Biomarkers in the post-surgery sample compared to the pre-surgery sample may indicate regression of NSCLC (for example, surgery successfully removed the lung tumor). Similar analyzes of biomarker levels can be carried out before and after other forms of treatment, such as before and after radiotherapy or administration of a therapeutic agent or a cancer vaccine.

In addition to testing biomarker levels as an independent diagnostic test, biomarker levels can also be performed along with the determination of SNPs or other genetic lesions or variability that are indicative of increased risk of disease susceptibility. (See, for example, Amos et al., Nature Genetics 40, 616-622 (2009)).

In addition to testing biomarker levels as an independent diagnostic assay, biomarker levels can also be performed in conjunction with radiological examination, such as CT scanning. For example, biomarkers can provide medical and economic justification for implementing CT scanning, such as for exploring large asymptomatic populations at risk of NSCLC (for example, smokers). For example, a “pre CT” trial of biomarker levels could be used to stratify high-risk individuals for CT scanning, such as to identify those at highest risk of NSCLC based on their biomarker levels and that should be prioritized for exploration. of TC. If a CT test is implemented, biomarker levels (for example, as determined by a serum or plasma sample aptamer test) of one or more biomarkers can be measured and the diagnostic score could be assessed together

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with additional biomedical information (for example, tumor parameters determined by CT test) to enhance the positive predictive value (PPV) with respect to CT or biomarker assay only. A panel of "post CT" aptamers to determine biomarker levels can be used to determine the probability that a pulmonary nodule observed by CT (or other imaging modality) is malignant or benign.

The detection of any of the biomarkers described herein may be useful for post CT assays. For example, biomarker assays can eliminate or reduce a significant number of false positive trials with respect to CT alone. In addition, biomarker trials can facilitate the treatment of patients. As an example, if a lung nodule is less than 5 mm in size, the results of biomarker trials can advance patients from "observation and waiting" to biopsy at an earlier time; if a lung nodule is 5-9 mm, biomarker assays can eliminate the use of a biopsy or thoracotomy in positive faso examinations; and if a lung nodule is larger than 10 mm, biomarker trials can eliminate surgery for a subpopulation of these patients with benign nodules. The elimination of the need for biopsy in some patients based on biomarker trials would be beneficial because there is significant morbidity associated with nodule biopsy and difficulty in obtaining nodule tissue depending on the location of the nodule. Similarly, eliminating the need for surgery in some patients, such as those whose nodules are in fact benign, would avoid unnecessary risks and costs associated with surgery.

In addition to testing biomarker levels along with radiological examination in high-risk individuals (for example, evaluating biomarker levels along with the size or other characteristics of a lung nodule or mass observed in an image capture scan), it can also be evaluated information regarding the biomarkers evaluated together with other types of data, particularly data indicating the risk of an individual of NSCLC (for example, patient's clinical history, occupational exposure history, symptoms, family history of cancer, risk factors such as if the individual was a smoker and / or the status of other biomarkers, etc.). This various data can be evaluated by automatic methods, such as a computer program / software, which can be incorporated into a computer or other device / device.

Any of the biomarkers described in image capture assays can also be used. For example, an image capture agent can be coupled to any of the described biomarkers, which can be used to aid in the diagnosis of NSCLC, to monitor the progression / remission of disease or metastasis, to monitor disease recurrence or to monitor disease. response to therapy, among other uses.

Detection and determination of biomarkers and biomarker values

A biomarker value for the biomarkers described herein can be detected using any of several known analytical methods. In one embodiment, a biomarker value is detected using a capture reagent. As used herein, a "capture agent" or "capture reagent" refers to a molecule that is capable of specifically binding with a biomarker. In various embodiments, the capture reagent may be exposed to the biomarker in solution or may be exposed to the biomarker while the capture reagent is immobilized on a solid support. In other embodiments, the capture reagent contains an element that is reactive with a secondary element in a solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution, and then the element in the capture reagent can be used together with the secondary element in the solid support to immobilize the biomarker in the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents include, but are not limited to, aptamers, antibodies, antigens, adnectins, anchyrins, other antibody mimetics and other protein frameworks, autoantibodies, chimeras, small molecules, an F (ab ') 2 fragment, a single chain antibody fragment , an Fv fragment, a single-chain Fv fragment, a nucleic acid, a lectin, a ligand binding receptor, antibodies, nanobodies, printed polymers, avimers, peptidomimetics, a hormone receptor, a cytokine receptor and synthetic receptors, and modifications and fragments of these.

In some embodiments, a biomarker value is detected using a biomarker / capture reagent complex.

In other embodiments, the biomarker value is obtained from the biomarker / capture reagent complex and is detected indirectly, such as, for example, as a result of a reaction that is subsequent to the biomarker / capture reagent interaction, but depends on the formation of the biomarker / capture reagent complex.

In some embodiments, the biomarker value is detected directly from the biomarker in a biological sample.

In one embodiment, the biomarkers are detected using a multiple format that allows simultaneous detection of two or more biomarkers in a biological sample. In an embodiment of the multiple format, capture reagents are immobilized, directly or indirectly, covalently or non-covalently, in discrete locations on a solid support. In another embodiment, a multiple format uses discrete solid supports in which each solid support has a single capture reagent associated with that solid support, such as, for example, quantum dots. In another embodiment, an individual device is used for the detection of each of multiple biomarkers for

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Detect in a biological sample. Individual devices can be configured to allow each biomarker in the biological sample to be processed simultaneously. For example, a microtiter plate can be used so that each well in the plate is used to uniquely analyze one of multiple biomarkers for detection in a biological sample.

In one or more of the above embodiments, a fluorescent label can be used to label a component of the biomarker / capture complex to allow the detection of the biomarker value. In various embodiments, the fluorescent label can be conjugated with a specific capture reagent for any of the biomarkers described herein using known techniques, and the fluorescent label can then be used to detect the corresponding biomarker value. Suitable fluorescent markers include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lysamine, phycoerythrin, Texas Red and others of such compounds.

In one embodiment, the fluorescent marker is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule includes at least one substituted indole ring system in which the carbon 3 substituent of the indole ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule includes an AlexaFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680 or AlexaFluor 700. In other embodiments, the dye molecule includes a first type and a second type of dye molecule, such as, for example, two different AlexaFluor molecules. In other embodiments, the dye molecule includes a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.

Fluorescence can be measured with various instrumentations compatible with a wide range of assay formats. For example, spectrofluorimeters have been designed to analyze microtiter plates, microscope slides, printed matrices, cuvettes, etc. See Principles of Fluorescence Spectroscopy, by J. R. Lakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

In one or more of the above embodiments, a chemiluminescence marker can be used to label a biomarker / capture complex component to allow the detection of a biomarker value. Suitable chemiluminescent materials include any of oxalyl chloride, Rhodamine 6G, Ru (bipi) 32+, TMAE (tetrakis (dimethylamino) ethylene), Pyrogallol (1,2,3-trihydroxybenzene), Lucigenin, peroxyoxalates, aryl oxalates, esters of acridinium, dioxetans and others.

In yet other embodiments, the detection method includes an enzyme / substrate combination that generates a detectable signal that corresponds to the biomarker value. In general, the enzyme catalyzes a chemical alteration of the chromogenic substrate that can be measured using various techniques, including spectrophotometry, fluorescence and chemiluminescence. Suitable enzymes include, for example, luciferases, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase and glucose-6-phosphate dehydrogenase, uricase , xanthine oxidase, lactoperoxidase, microperoxidase, and the like.

In yet other embodiments, the detection method may be a combination of fluorescence, chemiluminescence, radionuclide or enzyme / substrate combinations that generate a measurable signal. Multimodal signaling could have unique and advantageous characteristics in biomarker assay formats.

More specifically, the biomarker values for the biomarkers described herein can be detected using known analytical methods including, simple aptamer assays, multiple aptamer assays, single or multiple immunoassays, mRNA expression profiles, miRNA expression profiles, mass spectrometry analysis, histological / cytological methods, etc. as detailed below.

Determination of biomarker values using aptamer-based assays

Trials aimed at the detection and quantification of physiologically significant molecules in biological samples and other samples are important tools in scientific research and the field of health care. One class of such assays involves the use of a microarray that includes one or more aptamers immobilized on a solid support. The aptamers are each capable of binding with a target molecule in a highly specific manner and with very high affinity. See, for example, United States Patent No. 5,475,096 entitled "Nucleic Acid Ligands"; see also, for example, United States Patent No. 6,242,246, United States Patent No. 6,458,543 and United States Patent No. 6,503,715, each of which is entitled " Nucleic Acid Ligand Diagnostic Biochip ». Once the microarray is contacted with a sample, the aptamers bind with their respective target molecules present in the sample and thus allow a determination of a biomarker value corresponding to a biomarker.

As used herein, an "aptamer" refers to a nucleic acid that has a specific binding affinity for a target molecule. It is recognized that affinity interactions are a matter of degree;

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however, in this context, the "specific binding affinity" of an aptamer by its target means that the aptamer generally binds to its target with a much higher degree of affinity than if it binds to other components in a sample of test. An "aptamer" is a set of copies of a type or species of nucleic acid molecule having a particular nucleotide sequence. An aptamer can include any suitable number of nucleotides, including any number of chemically modified nucleotides. "Atamers" refers to more than one of said sets of molecules. Different aptamers may have the same or different number of nucleotides. The aptamers may be DNA or RNA or chemically modified nucleic acids and may be single stranded, double stranded or contain double stranded regions, and may include higher order structures. An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to covalently bind to its corresponding target. Any of the aptamer methods disclosed herein may include the use of two or more aptamers that specifically bind to the same target molecule. As described further below, an aptamer may include a marker. If an aptamer includes a marker, it is not necessary that all copies of the aptamer have the same marker. In addition, if different aptamers each include a marker, these different aptamers may have the same or a different marker.

An aptamer can be identified using any known method, including the SELEX process. Once identified, an aptamer can be prepared or synthesized according to any known method, including chemical synthetic methods and enzymatic synthetic methods.

As used herein, a "SOMAmer" or modified slow dissociation rate aptamer refers to an aptamer having improved dissociation rate characteristics. SOMAmers can be generated using the enhanced SELEX methods described in US publication No. 2009/0004667, entitled "Method for Generating Aptamers with Improved Off-Rates."

The terms "SELEX" and "SELEX process" are used interchangeably herein to refer in general to a combination of (1) the selection of aptamers that interact with a target molecule in a desirable manner, for example, high affinity binding to a protein, with (2) the amplification of the selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a specific target or biomarker.

In general, SELEX includes preparing a candidate mixture of nucleic acids, binding the candidate mixture to the desired target molecule to form an affinity complex, separating affinity complexes from unbound candidate nucleic acids, separating and isolating the nucleic acid from the complex. of affinity, purify the nucleic acid and identify a specific aptamer sequence. The process may include multiple cycles to further refine the affinity of the selected aptamer. The process may include amplification stages at one or more points in the process. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands." The SELEX process can be used to generate an aptamer that covalently binds to its target as well as an aptamer that binds non-covalently to its target. See, for example, U.S. Patent No. 5,705,337 entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX."

The SELEX process can be used to identify high affinity aptamers containing modified nucleotides that confer improved characteristics on the aptamer, such as, for example, improved administration characteristics or improved in vivo stability. Examples of such modifications include chemical substitutions at the base and / or ribose and / or phosphate positions. Aptamers identified with the SELEX process containing modified nucleotides are described in US Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides", which describes oligonucleotides containing chemically modified nucleotide derivatives in the 5 'and 2' positions of pyrimidines. U.S. Patent No. 5,580,737, see above, describes highly specific aptamers containing one or more nucleotides modified with 2'-amino (2-NH2), 2'-fluoro (2'F) and / or 2'O-methyl (2'-OMe). See also, United States patent application publication 2009/0098549, entitled "SELEX and PHOTOSELEX", which describes libraries of nucleic acids that have expanded chemical and physical properties and their use in SELEX and photoSELEX.

SELEX can also be used to identify aptamers that have desirable dissociation rate characteristics. See US Patent Application Publication 2009/0004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes improved SELEX methods for generating aptamers that can bind to target molecules. Methods for producing aptamers and photoaptamers having slower rates of dissociation of their respective target molecules are described. The methods involve contacting the candidate mixture with the target molecule, allowing the formation of target nucleic acid complexes to occur and performing a slow dissociation rate enrichment process where the nucleic acid target complexes with rapid dissociation rates dissociate and do not re-form, while complexes with a slow dissociation rate remain intact. Additionally, the methods include the use of modified nucleotides in the production of candidate nucleic acid mixtures to generate aptamers with improved dissociation rate performance.

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A variation of this assay employs aptamers that include photoreactive functional groups that allow aptamers to covalently bind or "photoreticulate" with their target molecules. See, for example, US Patent No. 6,544,776 entitled "Nucleic Acid Ligands Diagnostic Biochip". These photoreactive aptamers are also called photoaptamers. See, for example, United States Patent No. 5,763,177, United States Patent No. 6,001,577 and United States Patent No. 6,291,184, each of which is entitled " Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX »; see also, for example, U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands." After the microarray has come into contact with the sample and the photoaptamers have had the opportunity to bind with their target molecules, the photoattamers are photoactivated and the solid support is washed to remove any non-specific bound molecule. Rigorous washing conditions can be used, since the target molecules that bind to photoaptamers in general are not removed, due to covalent bonds created by the group or functional groups in photoaptamers. In this way, the assay allows the detection of a biomarker value corresponding to a biomarker in the test sample.

In both of these assay formats, aptamers are immobilized on the solid support before contacting the sample. In certain circumstances, however, immobilization of aptamers before contact with the sample may not provide an optimal assay. For example, pre-immobilization of the aptamers can result in an ineffective mixing of the aptamers with the target molecules on the surface of the solid support, which leads perhaps to long reaction times and, therefore, prolonged incubation periods to allow The effective binding of aptamers with their target molecules. In addition, when photoaptamers are used in the test and depending on the material used as a solid support, the solid support tends to disperse or absorb the light used to effect the formation of covalent bonds between the photoaptamers and their target molecules. In addition, depending on the method used, the detection of target molecules bound to their aptamers may be subject to imprecision, since the surface of the solid support may also be exposed to and affected by any labeling agent that is used. Finally, immobilization of aptamers in the solid support generally involves a stage of preparation of aptamers (i.e. immobilization) before exposure of the aptamers to the sample, and this stage of preparation may affect the activity or functionality of the aptamers.

Aptamer assays have also been described that allow an aptamer to capture its target in solution and then employ separation steps that are designed to remove specific components of the aptamer-target mixture before detection (see the patent application publication of the United States 2009/0042206, entitled "Multiplexed Analyzes of Test Samples"). The aptamer assay methods described allow the detection and quantification of a target other than nucleic acid (eg, a protein target) in a test sample by detecting and quantifying a nucleic acid (i.e., an aptamer). The methods described create a nucleic acid substitute (i.e., the aptamer) to detect and quantify a target other than nucleic acid, thereby allowing the wide variety of nucleic acid technologies, including amplification, to be applied to a wide range. of desired targets, including protein targets.

Aptamers can be constructed to facilitate separation of the test components of an aptamer biomarker complex (or covalent complex of photoatamer biomarker) and allow isolation of the aptamer for detection and / or quantification. In one embodiment, these constructs may include a cleavable or releasable element in the aptamer sequence. In other embodiments, additional functionality may be introduced into the aptamer, for example, a labeled or detectable component, a spacer component or a specific binding marker or immobilization element. For example, the aptamer can include a marker connected to the aptamer by a cleavable moiety, a marker, a spacer component that separates the marker and the cleavable moiety. In one embodiment, a cleavable element is a photo cleavable linker. The cleavable linker can bind with a biotin moiety and a spacer section, can include an NHS group for amine derivatization, and can be used to introduce a biotin group into an aptamer, thereby allowing release of the aptamer later in The test method.

Homogeneous tests, performed with all test components in solution, do not require separation of sample and reagents before signal detection. These methods are quick and easy to use. These methods generate signal based on a capture or molecular binding reagent that reacts with its specific target. For NSCLC, the molecular capture reagents would be an aptamer or an antibody or the like and the specific target would be a NSCLC biomarker of Table 1.

In one embodiment, a method for signal generation takes advantage of the anisotropy signal change due to the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture reacts with its target, the increased molecular weight causes the rotational movement of the fluorophore bound to the complex to become much slower by changing the anisotropy value. By monitoring the change of anisotropy, binding events can be used to quantitatively measure biomarkers in solutions. Other methods include fluorescence polarization assays, molecular beacon methods, time-resolved fluorescence disruption, chemiluminescence, fluorescence resonance energy transfer, and the like.

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An exemplary solution based aptamer assay that can be used to detect a biomarker value corresponding to a biomarker in a biological sample includes the following: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first marker and has a specific affinity for the biomarker, where an aptamer affinity complex is formed when the biomarker is present in the sample; (b) exposing the mixture to a first solid support that includes a first capture element and allow the first marker to be associated with the first capture element; (c) removing any component from the mixture not associated with the first solid support; (d) linking a second marker with the biomarker component of the aptamer affinity complex; (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support that includes a second capture element and allowing the second marker to be associated with the second capture element; (g) removing any non-complex aptamer from the mixture by separating the non-complex aptamer from the aptamer affinity complex; (h) elute the solid support aptamer; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

Any means known in the art can be used to detect a biomarker value by detecting the aptamer component of an aptamer affinity complex. Several different detection methods can be used to detect the aptamer component of an affinity complex, such as, for example, hybridization assays, mass spectroscopy or QPCR. In some embodiments, nucleic acid sequencing methods can be used to detect the aptamer component of an aptamer affinity complex and thereby detect a biomarker value. Briefly, a test sample can be subjected to any type of nucleic acid sequencing method to identify and quantify the sequence or sequences of one or more aptamers present in the test sample. In some embodiments, the sequence includes the entire aptamer molecule or any part of the molecule that can be used to uniquely identify the molecule. In other embodiments, identification sequencing is a specific sequence added to the aptamer; such sequences are often referred to as "markers", "bar codes" or "postal codes". In some embodiments, the sequencing method includes enzymatic steps to amplify the aptamer sequence or convert any type of nucleic acid, including RNA and DNA that contain chemical modifications at any position, into any other type of nucleic acid suitable for sequencing.

In some embodiments, the sequencing method includes one or more cloning steps. In other embodiments, the sequencing method includes a direct sequencing method without cloning.

In some embodiments, the sequencing method includes a targeted approach with specific primers that target one or more aptamers in the test sample. In other embodiments, the sequencing method includes a randomized approach that addresses all aptamers in the test sample.

In some embodiments, the sequencing method includes enzymatic steps to amplify the target molecule for sequencing. In other embodiments, the sequencing method directly sequences individual molecules. An exemplary nucleic acid sequencing method that can be used to detect a biomarker value corresponding to a biomarker in a biological sample includes the following: (a) converting a mixture of aptamers containing chemically modified nucleotides into non-nucleic acids modified with an enzymatic stage; (b) randomly sequencing the resulting unmodified nucleic acids with a massive parallel sequencing platform such as, for example, the sequencing system 454 (454 Life Sciences / Roche), the Illumina (Illumina) sequencing system, the ABI SOLiD (Applied Biosystems) sequencing system, the HeliScope (Helicos Biosciences) individual molecule sequencer, or the Pacific Biosciences (Pacific BioSciences) real-time individual molecule sequencing system or the Polonator G (Dover Systems) sequencing system; and (c) identify and quantify the aptamers present in the mixture by specific sequence and sequence count.

Determination of biomarker values using immunoassays

Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format. To improve the specificity and sensitivity of an assay method based on immunoreactivity, monoclonal antibodies are frequently used because of their specific epitope recognition. Polyclonal antibodies have also been used successfully in various immunoassays due to their increased affinity for the target compared to monoclonal antibodies. Immunoassays have been designed for use with a wide array of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative and quantitative results.

Quantitative results are generated by using a standard curve created with known concentrations of the specific analyte to detect. The response or signal of an unknown sample is represented in the standard curve and an amount or value corresponding to the target is established in the unknown sample.

Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of

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an analyte This method is based on the binding of a label with the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme. ELISA assays can be formatted for direct, indirect, competitive or sandwich analyte detection. Other methods are based on markers such as, for example, radioisotopes (1125) or fluorescence. Additional techniques include, for example, algutination, nephelometry, turbidimetry, Western blotting, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, serology, Luminex assay and others (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence and fluorescence resonance energy transfer (FRET) or time-resolved FRET immunoassay (TR-FRET). Examples of methods for detecting biomarkers include immunoprecipitation of biomarkers followed by quantitative methods that allow differentiation of peptide size and level, such as gel electrophoresis, capillary electrophoresis, flat electrochromatography and the like.

The methods of detection and / or quantification of a detectable marker or signal generating material depend on the nature of the marker. The products of reactions catalyzed by appropriate enzymes (where the detectable label is an enzyme; see above) may be, without limitation, fluorescent, luminescent or radioactive or may absorb visible or ultraviolet light. Examples of suitable detectors for detecting such detectable markers include, without limitation, radiographic film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers and densitometers.

Any of the methods for detection can be performed in any format that allows for any preparation, processing and proper analysis of the reactions. This can be, for example, in multiwell test plates (for example, 96 wells or 384 wells) or using any suitable matrix or microarray. Stock solutions for various agents can be performed manually or roboticly, and all pipetting, dilution, mixing, distribution, washing, incubation, sample reading, data collection and subsequent analysis can be performed robotic form using commercially available analysis software, robotics and detection instrumentation capable of detecting a detectable marker.

Determination of biomarker values using gene expression profiles

The mRNA measurement in a biological sample can be used as a substitute for the detection of the level of the corresponding protein in the biological sample. Therefore, any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA.

MRNA expression levels are measured by quantitative reverse transcription polymerase chain reaction (RT-PCR followed by qPCR). RT-PCR is used to create a cDNA from mRNA. The cDNA can be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. Compared to a standard curve, qPCR can produce an absolute measurement such as number of mRNA copies per cell. Northern blots, microarrays, Invader assays and RT-PCR combined with capillary electrophoresis have all been used to measure mRNA expression levels in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

The miRNA molecules are small RNAs that are not coding but can regulate gene expression. Any of the suitable methods for measuring mRNA expression levels for the corresponding mRNA can also be used. Recently many laboratories have investigated the use of miRNA as biomarkers for disease. Many diseases involve regulation of generalized transcription and it is not surprising that miRNAs could find a role as biomarkers. The relationship between miRNA concentrations and disease is often even less clear than the relationships between protein levels and disease, but the value of miRNA biomarkers could be substantial. Of course, as with any RNA differentially expressed during the disease, the problems facing the development of an in vitro diagnostic product will include the requirement that miRNAs survive in the diseased cell and are easily removed for analysis or that the miRNAs are released into blood or other matrices where they must survive long enough to be measured. Protein biomarkers have similar requirements, although many potential protein biomarkers are intentionally secreted at the site of pathology and function, during disease, in a paracrine manner. Many potential protein biomarkers are designed to act outside the cells within which those proteins are synthesized.

Biomarker detection using in vivo molecular image capture technologies

Any of the biomarkers described (see Table 1) can also be used in molecular image capture assays. For example, an image capture agent can be coupled to any of the described biomarkers, which can be used to aid in the diagnosis of NSCLC, to monitor the progression / remission of disease or metastasis, to monitor disease recurrence or to monitor disease. response to therapy, among other uses.

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In vivo image capture technologies provide non-invasive methods to determine the status of a particular disease in an individual's body. For example, whole parts of the body, or even the whole body, can be seen as a three-dimensional image, thus providing valuable information regarding morphology and structures in the body. Such technologies may be combined with the detection of the biomarkers described herein to provide information regarding the status of cancer, in particular the status of NSCLC, of an individual.

The use of in vivo molecular imaging technologies is expanding due to various advances in technology. These advances include the development of new contrast agents or markers, such as radiomarkers and / or fluorescent markers, which can provide strong signals in the body; and the development of new powerful image capture technology, which can detect and analyze these signals from outside the body, with sufficient sensitivity and accuracy to provide useful information. The contrast agent can be visualized in an appropriate image capture system, thereby providing an image of the part or parts of the body in which the contrast agent is located. The contrast agent can be linked with or associated with a capture reagent, such as an aptamer or an antibody, for example, and / or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression) or a complex that contains any of these with one or more macromolecules and / or other particulate forms.

The contrast agent may also have a radioactive atom that is useful in capturing images. Suitable radioactive atoms include technetium 99m or iodine 123 for scintigraphic studies. Other readily detectable moieties include, for example, spin markers for magnetic resonance imaging (MRI) such as, for example, iodine 123 again, iodine 131, indium 111, fluorine 19, carbon 13, nitrogen 15, oxygen 17 , gadolinium, manganese or iron. Such markers are well known in the art and a person skilled in the art could easily select them.

Conventional image capture techniques include but are not limited to magnetic resonance imaging, computed tomography scanning, positron emission tomography (PET), individual photon emission computed tomography (SPECT) and the like. For in vivo diagnostic imaging, the type of detection instrument available is an important factor in the selection of a given contrast agent, such as a given radionuclide and the particular biomarker that is used for target (protein, mRNA and the like ). The radionuclide chosen normally has a type of degradation that is detectable by a given type of instrument. In addition, when a radionuclide is selected for in vivo diagnosis, its half-life should be long enough to allow detection at the time of maximum uptake by the target tissue but short enough for the host's deleterious radiation to be minimized.

Exemplary image capture techniques include but are not limited to TEP and SPECT, which are image capture techniques in which a radionuclide is synthetically and locally administered to an individual. Subsequent uptake of the radio indicator is measured over time and is used to obtain information about the target tissue and the biomarker. Due to the high energy emissions (gamma rays) of the specific isotopes used and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity can be inferred from outside the body.

Positron emitting nuclides commonly used in PET include, for example, carbon 11, nitrogen 13, oxygen 15 and fluorine 18. Isotopes that are degraded by electron capture and / or gamma emission are used in SPECT and include, for example, iodine 123 and technetium 99m. An exemplary method for marking amino acids with 99m technetium is the reduction of pertechnetate ion in the presence of a chelating precursor to form the 99m labile technetium-precursor complex, which, in turn, reacts with the metal-binding group of a bifunctional modified chemotactic peptide to form a 99m technetium-chemotactic peptide conjugate.

Antibodies are frequently used for such diagnostic imaging methods in vivo. The preparation and use of antibodies for in vivo diagnosis is well known in the art. Marked antibodies that specifically bind to any of the biomarkers in Table 1 can be injected into an individual suspected of having a certain type of cancer (e.g., NSCLC), detectable according to the particular biomarker used, for the purpose of diagnosing or Evaluate the disease status of the individual. The marker used will be selected according to the image capture mode to use, as previously described. The location of the marker allows the determination of the spread of cancer. The amount of marker within an organ or tissue also allows the determination of the presence or absence of cancer in that organ or tissue.

Similarly, aptamers can be used for said diagnostic imaging methods in vivo. For example, an aptamer that was used to identify a particular biomarker described in Table 1 (and therefore specifically binds with that particular biomarker) can be properly labeled and injected into an individual suspected of having NSCLC, detectable according to the particular biomarker, in order to diagnose or evaluate the individual's NSCLC status. The marker used will be selected according to the image capture mode to use, as previously described. The location of the marker allows the determination of the spread of cancer. The amount of marker within an organ or tissue also allows

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the determination of the presence or absence of cancer in that organ or tissue. Image capture agents targeting aptamers could have unique and advantageous characteristics related to tissue penetration, tissue distribution, kinetics, elimination, potency and selectivity compared to other image capture agents.

Such techniques can also optionally be performed with labeled oligonucleotides, for example, for detection of gene expression by image capture with antisense oligonucleotides. These methods are used for in situ hybridization, for example, with fluorescent molecules or radionuclides as the label. Other methods for detection of gene expression include, for example, detection of the activity of an indicator gene.

Another general type of image capture technology is optical image capture, in which fluorescent signals are detected in the subject by an optical device that is external to the subject. These signals may be due to real fluorescence and / or bioluminescence. Improvements in the sensitivity of optical detection devices have increased the utility of optical image capture for in vivo diagnostic tests.

The use of in vivo molecular biomarker imaging is increasing, including for clinical trials, for example, to more quickly measure clinical efficacy in trials for new cancer therapies and / or to avoid prolonged placebo treatment for diseases. , such as multiple sclerosis, in which such prolonged treatment can be considered ethically questionable.

For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

Determination of biomarker values using histology / cytology methods

For evaluation of NSCLC, various tissue samples can be used in histological or cytological methods. Sample selection depends on the location of the primary tumor and the sites of metastasis. For example, endo and transbronchial biopsies, fine needle aspirates, cutting needles and thick needle biopsies for histology can be used. Bronchial washing and brushing, pleural aspiration, pleural fluid and sputum for cytology can be used. Although cytological analysis is still used in the diagnosis of NSCLC, histological methods are known to provide better sensitivity for cancer detection. Any of the biomarkers identified herein that have been shown to be positively regulated (Table 1) in individuals with NSCLC can be used to stain a histological test sample with an indication of disease.

In one embodiment, one or more specific capture reagents for the biomarker or corresponding biomarkers are used in a cytological evaluation of a sample of lung tissue cells and may include one or more of the following: collect a sample of cells, fix the cell sample, dehydrate, clarify, immobilize the cell sample on a microscope slide, permeabilize the cell sample, treat for analyte recovery, stain, fade, wash, block and react with one or more capture reagents in one buffered solution In another embodiment, the cell sample is produced from a block of cells.

In another embodiment, one or more specific capture reagents for the biomarker or corresponding biomarkers are used in a histological evaluation of a sample of lung tissue and may include one or more of the following: collect a tissue test sample, fix the sample of tissue, dehydrate, rinse, immobilize the tissue sample on a microscope slide, permeabilize the tissue sample, treat for analyte recovery, stain, fade, wash, block, rehydrate and react with reagent or capture reagents in a buffered solution In another embodiment, the fixation and dehydration are replaced with freezing.

In another embodiment, the specific biomarker or corresponding aptamers or biomarkers are reacted with the histological or cytological sample and can act as the nucleic acid target in a nucleic acid amplification method. Suitable nucleic acid amplification methods include, for example, PCR, q-beta replicase, rolling circle amplification, chain shift, helicase dependent amplification, loop-mediated isothermal amplification, ligase chain reaction and circle amplification wheeler assisted by restriction and circularization.

In one embodiment, the specific capture reagents or reagents for the corresponding biomarkers for use in histological or cytological evaluation are mixed in a buffered solution that may include any of the following: blocking materials, competitors, detergents, stabilizers, nucleic acid vehicle, polyanionic materials, etc.

A "cytology protocol" generally includes sample collection, sample fixation, sample immobilization and staining. The "cell preparation" may include several processing steps after sample collection, including the use of one or more slow dissociation rate aptamers for staining the prepared cells.

Sample collection may include placing the sample directly in an untreated transport container, placing the sample in a transport container containing some type of medium, or placing the sample

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directly on a slide (immobilization) without any treatment or fixation.

Sample immobilization can be improved by applying a portion of the collected test sample to a glass slide that is treated with polylysine, gelatin or a silane. Slides can be prepared by spreading a thin and uniform layer of cells along the slide. General care is taken to minimize mechanical distortion and drying devices. Liquid test samples can be processed in a cell block method. Or, alternatively, 1: 1 liquid test samples can be mixed with the fixative solution for approximately 10 minutes at room temperature.

Cell blocks can be prepared from residual effusions, sputum, urine sediments, gastrointestinal fluids, lung fluids, cell scrapes or fine needle aspirates. The cells are concentrated or packed by centrifugation or membrane filtration. Several methods for the preparation of cell blocks have been developed. Representative procedures include methods of set sediment, bacterial agar or membrane filtration. In the fixed sediment method, the cell sediment is mixed with a fixative such as Bouins, picric acid or buffered formalin and then the mixture is centrifuged to sediment the fixed cells. The supernatant is removed, drying the cell pellet as completely as possible. The sediment is collected and wrapped in optical paper and then placed in a tissue cassette. The tissue cassette is placed in a jar with an additional fixative and processed as a tissue sample. The agar method is very similar but the sediment is removed and dried on a paper towel and then cut in half. The cut side is placed in a drop of molten agar on a glass slide and then the sediment is covered with agar ensuring that no bubbles form on the agar. The agar is allowed to harden and then any excess agar is trimmed. This is placed in a tissue cassette and the tissue process is completed. Alternatively, the sediment can be suspended directly in 2% liquid agar at 65 ° C and the sample centrifuged. The agar cell pellet is allowed to solidify for one hour at 4 ° C. The solid agar can be removed from the centrifuge tube and cut in half. The agar is wrapped in filter paper and then the tissue cassette. Processing from this point forward is as described above. Centrifugation can be replaced in any of these procedures with membrane filtration. Any of these processes can be used to generate a "cell block sample."

Cell blocks can be prepared using specialized resin including Lowicryl, LR White, LR Gold, Unicryl and MonoStep resins. These resins have low viscosity and can be polymerized at low temperatures and with ultraviolet (UV) light. The inclusion process is based on the progressive cooling of the sample during dehydration, the transfer of the sample to the resin and the polymerization of a block at the final low temperature at the appropriate UV wavelength.

Cell block sections can be stained with hematoxylin-eosin for cytomorphological examination while additional sections are used for the examination of specific markers.

Be it the cytological or histological process, the sample can be fixed before further processing to avoid degradation of samples. This process is called "fixation" and describes a wide range of materials and procedures that can be used interchangeably. The sample and reagent fixation protocol are best selected empirically based on the targets to be detected and the cell / tissue type to analyze. Sample fixation is based on reagents such as ethanol, polyethylene glycol, methanol, formalin or isopropanol. Samples should be fixed as soon as possible after collection and fixation to the slide. However, the selected fixative can introduce structural changes in various molecular targets making its subsequent detection more difficult. The fixation and immobilization processes and their sequence can modify the appearance of the cell and these changes must be anticipated and recognized by the cytotechnologist. Fixatives can cause shrinkage of certain cell types and cause the cytoplasm to appear granular or reticular. Many fixatives act by crosslinking cellular components. This can damage or modify specific epitopes, generate new epitopes, cause molecular associations and reduce membrane permeability. Formalin fixation is one of the most common cytological / histological approaches. Formalin forms methyl bonds between adjacent proteins or within proteins. Precipitation or coagulation is also used for fixation and ethanol is frequently used in this type of fixation. A combination of cross-linking and precipitation can also be used for fixation. A strong fixation process is better for preserving morphological information while a weaker fixation process is better for the conservation of molecular targets.

A representative fixative is 50% absolute ethanol, 2 mM polyethylene glycol (PEG), 1.85% formaldehyde. Variations of this formulation include ethanol (50% to 95%), methanol (20% - 50%) and formalin (formaldehyde) only. Another common fixative is PEG 1500 2%, 50% ethanol and 3% methanol. The slides are placed in the fixative for approximately 10 to 15 minutes at room temperature and then removed and allowed to dry. Once the slides are fixed, they can be rinsed with a buffered solution such as PBS.

A wide range of dyes can be used to differentially highlight and contrast or "dye" cellular, subcellular and tissue elements or morphological structures. Hematoxylin is used to stain the nuclei of a blue or black color. Both Orange G-6 and Eosin Azure stain the cell cytoplasm. Orange G stains cells that contain keratin and yellow glycogen. Eosin Y is used to stain nucleoli, cilia, red blood cells and cells

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squamous superficial epithelial. Romanowsky strains are used for air-dried slides and are useful for enhancing pleomorphism and distinguishing extracellular material from intracytoplasmic.

The staining process may include a treatment to increase the permeability of the cells to staining. Treatment of cells with a detergent can be used to increase permeability. To increase cell and tissue permeability, the fixed samples can be further treated with solvents, saponins or non-ionic detergents. Enzymatic digestion can also improve the accessibility of specific targets in a tissue sample.

After staining, the sample is dehydrated using a succession of alcohol washes with an increasing alcohol concentration. The final wash is done with xylene or a xylene substitute, such as a citrus terpene, which has a refractory index close to that of the coverslip to apply to the slide. This final stage is called clearance. Once the sample has been dehydrated and rinsed, a mounting medium is applied. The mounting means is selected to have a refractory index close to the glass and is capable of joining the coverslip with the slide. It will also inhibit further drying, shrinking or discoloration of the cell sample.

Regardless of the stains or processing used, the final evaluation of the lung cytological test sample is performed by some type of microscope to allow a visual inspection of the morphology and a determination of the presence or absence of the marker. Exemplary microscopic methods include bright field, phase contrast, fluorescence and differential interference contrast.

If secondary tests are needed on the sample after the test, the coverslip can be removed and the slide faded. The allocation involves using the original solvent systems used in staining the slide originally without the dye added and in a reverse order to the original staining procedure. The allocation can also be completed by soaking the slide in acidic alcohol until the cells are colorless. Once the slides are colorless they rinse well in a water bath and the second staining procedure is applied.

In addition, specific molecular differentiation may be possible in conjunction with cellular morphological analysis through the use of specific molecular reagents such as antibodies or nucleic acid probes or aptamers. This improves the accuracy of diagnostic cytology. Microdissection can be used to isolate a subset of cells for further evaluation, in particular, for genetic evaluation of abnormal chromosomes, gene expression or mutations.

The preparation of a tissue sample for histological evaluation involves fixation, dehydration, infiltration, inclusion and section. Fixation reagents used in histology are very similar or identical to those used in cytology and have the same problems of preservation of morphological elements at the expense of molecular molecules such as individual proteins. Time can be saved if the sample holder is not fixed and hydrated but instead frozen and then frozen sectioned. This is a smoother processing procedure and can retain more individual markers. However, freezing is not acceptable for long-term storage of a tissue sample since subcellular information is lost due to the introduction of ice crystals. The ice in the frozen tissue sample also prevents the sectioning process from producing a very fine cut and therefore some microscopic resolution and image capture of subcellular structures can be lost. In addition to formalin fixation, osmium tetroxide is used to fix and stain phospholipids (membranes).

Dehydration of tissues is achieved with successive washes of increasing alcohol concentration. The clearance employs a material that is miscible with alcohol and the inclusion material and involves a stepwise process that starts with alcohol: 50:50 rinse reagent and then 100% rinse agent (xylene or xylene substitute). Infiltration involves incubating the tissue with a liquid form of the inclusion agent (hot wax, nitrocellulose solution) first to inclusion agent: 50:50 clearing agent and then 100% inclusion agent. The inclusion is completed by placing the tissue in a mold or cassette and filling with molten inclusion agent such as wax, agar or gelatin. The inclusion agent is allowed to harden. The hardened tissue sample can then be placed in thin section for staining and further examination.

Before staining, the tissue section is deparaffinized and rehydrated. Xylene is used to dewax the section, one or more changes of xylene can be used and the tissue is rehydrated by successive washes in alcohol of decreasing concentration. Before dewaxing, the tissue section can be immobilized by heat on a glass slide at approximately 80 ° C for approximately 20 minutes.

Laser capture microdissection allows the isolation of a subset of cells for further analysis of a tissue section.

As in cytology, to enhance the visualization of microscopic elements, the tissue section or the cut can be stained with various stains. A large menu of commercially available stains can be used to enhance or identify specific elements.

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To further increase the interaction of molecular reagents with cytological / histological samples, several techniques for "analyte recovery" have been developed. The first of these techniques uses high temperature heating of a fixed sample. This method is also called heat-induced epitope recovery or REIC. Various heating techniques have been used, including steam heating, microwave, autoclave, water baths and pressure cooking or a combination of these heating methods. Analyte recovery solutions include, for example, water, citrate and normal saline buffers. The key to the recovery of analytes is time at high temperature but lower temperatures have also been used successfully for longer times. Another key to analyte recovery is the pH of the heating solution. It has been found that low pH provides the best immunostaining but also results in funds that often require the use of a second tissue section as a negative control. The most uniform benefit (increased immunostaining without increasing the background) is generally obtained with a high pH solution regardless of the composition of the buffer. The analyte recovery process for a specific target is empirically optimized for the target using heat, time, pH and buffer composition as variables for process optimization. The use of the microwave analyte recovery method allows sequential staining of different targets with antibody reagents. However, it has also been shown that the time required to achieve antibody and enzyme complexes between the staining stages degrades cell membrane analytes. Microwave heating methods have also improved in situ hybridization methods.

To start the analyte recovery process, the section is first dewaxed and hydrated. The slide is then placed in 10 mM sodium citrate buffer pH 6.0 on a plate or bottle. A representative procedure uses a microwave at 1100 W and microwaves the slide at 100% power for 2 minutes followed by microwave heating of the slides using 20% power for 18 minutes after checking to ensure that the slide remains covered with liquid. The slide is then allowed to cool in the uncovered container and then rinsed with distilled water. REIC can be used in combination with an enzymatic digestion to improve the reactivity of the target to immunochemical reagents.

One of said enzymatic digestion protocols uses proteinase K. A concentration of 20 g / ml of proteinase K is prepared on the basis of 50 mM Tris, 1 mM EDTA, 0.5% Triton X-100, pH 8.0 buffer. The process involves first dewaxing sections in 2 xylene changes, of 5 minutes each. The sample is then hydrated in 2 changes of 100% ethanol for 3 minutes each, 95% ethanol and 80% for 1 minutes each and then rinsed in distilled water. The sections are covered with working proteinase K solution and incubated for 10-20 minutes at 37 ° C in a humidified chamber (the optimal incubation time may vary depending on the type of tissue and degree of fixation). The sections are cooled to room temperature for 10 minutes and then rinsed in PBS Tween 20 for 2x2 min. If desired, sections can be blocked to eliminate potential interference from endogenous compounds and enzymes. The section is then incubated with primary antibody at appropriate dilution in primary antibody dilution buffer for 1 hour at room temperature or overnight at 4 ° C. The section is then rinsed with PBS Tween 20 for 2x2 min. Additional blocking may be performed, if necessary for the specific application, followed by further rinsing with PBS Tween 20 for 3x2 min and then the immunostaining protocol is finally completed.

It has also been shown that a simple treatment with 1% SDS at room temperature improves immunohistochemical staining. Analyte recovery methods have been applied to slide mounted sections as well as floating sections. Another treatment option is to place the slide in a bottle containing citric acid and Nonident P40 0.1 at pH 6.0 and heat to 95 ° C. The slide is then washed with a buffer solution such as PBS.

For immunological staining of tissues it may be useful to block the non-specific association of the antibody with tissue proteins by soaking the section in a protein solution such as whey or skimmed milk powder.

Blocking reactions may include the need to reduce the level of endogenous biotin; eliminate effects of endogenous loading; inactivate endogenous nucleases; and / or inactivate endogenous enzymes such as peroxidase and alkaline phosphatase. Endogenous nucleases can be inactivated by degradation with proteinase K, by heat treatment, use of a chelating agent such as EDTA or EGTA, the introduction of DNA or carrier RNA, treatment with a chaotrope such as urea, thiourea, guanidine hydrochloride, thiocyanate guanidine, lithium perchlorate, etc., or diethyl pyrocarbonate. The alkaline phosphatase can be inactivated by 0.1 N HCl for 5 minutes at room temperature or treatment with 1 mM levamisole. The peroxidase activity can be eliminated by treatment with 0.03% hydrogen peroxide. Endogenous biotin can be blocked by soaking the slide or section in an avidin solution (it can be substituted with streptavidin, neutravidin) for at least 15 minutes at room temperature. The slide or section is then washed for at least 10 minutes in buffer. This can be repeated at least three times. Then the slide or section is soaked in a biotin solution for 10 minutes. This can be repeated at least three times with a new biotin solution each time. The buffer wash procedure is repeated. The blocking protocols should be minimized to avoid damaging the cellular or tissue structure or the target or targets of interest but one or more of these protocols could be combined to "block" a slide or section before the reaction with one or more velocity aptamers from

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slow dissociation See Basic Medical Histology: the Biology of Cells, Tissues and Organs, written by Richard G. Kessel, Oxford University Press, 1998.

Determination of biomarker values using mass spectrometry methods

Various configurations of mass spectrometers can be used to detect biomarker values. Various types of mass spectrometers are available or can be produced with various configurations. In general, a mass spectrometer has the following main components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system and instrument control system and a data system. The difference in the sample input, the ion source and the mass analyzer generally defines the type of instrument and its capabilities. For example, an input can be a source of liquid chromatography on a capillary column or it can be a direct probe or platform as used in matrix-assisted laser desorption. Typical ion sources are, for example, electrospray, including nanopulverization and micropulverization or matrix assisted laser desorption. Typical mass analyzers include a quadrupole mass filter, ion trap mass analyzer and flight time mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70: 647 R-716R (1998); Kinter and Sherman, New York (2000)).

Protein biomarkers and biomarker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS) n, desorption mass spectrometry and laser-assisted flight-time ionization (MALDI - TOF - MS), surface-time-enhanced laser desorption / ionization mass spectrometry (SELDI-TOF-MS), silicon desorption / ionization (GOD ), secondary ion mass spectrometry (SIMS), quadrupole-flight time (Q-TOF), tandem flight time technology (TOF / TOF), called TOF / TOF ultraflex III, chemical ionization mass spectrometry a atmospheric pressure (APCI-MS), APCI-MS / MS, APCI- (MS) N, atmospheric pressure photoionization mass spectrometry (aPpI-MS), APPI-MS / MS and APPI- (ms) n, spectrometry quadrupole masses, Fourier transform mass spectrometry (FTMS), spectromet quantitative mass laugh and ionic trap mass spectrometry.

Sample preparation strategies are used to label and enrich samples before spectroscopic characterization of protein biomarker masses and determination of biomarker values. Labeling methods include, but are not limited to, isobaric marker for relative and absolute quantification (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples with respect to candidate biomarker proteins prior to mass spectroscopic analysis include, but are not limited to, aptamers, antibodies, nucleic acid probe, chimeras, small molecules, an F (ab ') 2 fragment, a single stranded antibody fragment, an Fv fragment, a single stranded Fv fragment, a nucleic acid, a lectin, a ligand binding receptor, antibodies, nanobodies, anchyrins, domain antibodies, alternative antibody frameworks (e.g. diabody, etc. ), printed polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, treose nucleic acid, a hormone receptor, a cytokine receptor and synthetic receptors, and modifications and fragments thereof.

Determination of biomarker values using a proximity linkage assay

A proximity linkage assay can be used to determine biomarker values. Briefly, a test sample is contacted with a pair of affinity probes that can be a pair of antibodies or a pair of aptamers, with each member of the extended pair with an oligonucleotide. The targets for the pair of affinity probes can be two different ones determined in one protein or one determined in each of two different proteins, which can exist as homo or heteromultimeric complexes. When the probes bind to the specific target, the free ends of the oligonucleotide extensions are placed in close enough proximity to hybridize with each other. Hybridization of oligonucleotide extensions is facilitated by a common connector oligonucleotide that acts to bind oligonucleotide extensions together when they are close enough. Once the oligonucleotide extensions of the probes have hybridized, the ends of the extensions are joined together by enzymatic DNA ligation.

Each oligonucleotide extension comprises a primer site for PCR amplification. Once the oligonucleotide extensions have been linked to each other, the oligonucleotides form a continuous DNA sequence that, by PCR amplification, reveals information regarding the identity and quantity of the target protein, as well as information regarding protein interactions. -protein when certain targets are in two different proteins. Proximity ligation can provide a highly sensitive and specific assay for protein concentration and interaction information in real time by using real-time PCR. Probes that do not bind with those of interest do not have the corresponding oligonucleotide extensions placed in proximity and no linkage or amplification by PCR can occur, which results in no signal being produced.

The previous tests allow the detection of biomarker values that are useful in methods for

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diagnose NSCLC, where the methods comprise detecting, in a biological sample of an individual, at least N values of biomarkers that each correspond to a biomarker selected from the group consisting of the biomarkers provided in Table 1, where a classification, such as It is described in detail later, using biomarker values indicates whether the individual has NSCLC. Although certain described NSCLC biomarkers are useful alone for detecting and diagnosing NSCLC, methods for grouping multiple subsets of the NSCLC biomarkers that are each useful as a panel of three or more biomarkers are also described herein. Therefore, various embodiments of the present application provide combinations comprising N biomarkers, wherein N is at least three biomarkers. In other embodiments, N is selected to be any number of 2-59 biomarkers. It will be appreciated that N can be selected to be any number of any of the ranges described above, as well as similar, but higher order intervals. According to any of the methods described herein, biomarker values can be detected and classified individually or can be detected and classified collectively, as for example in a multiple assay format.

In another aspect, methods are provided to detect an absence of NSCLC, the methods comprising detecting, in a biological sample of an individual, at least N biomarker values that each correspond to a biomarker selected from the group consisting of the biomarkers provided in Table 1, where a classification, as described in detail below, of the biomarker values indicates an absence of NSCLC in the individual. Although certain described NSCLC biomarkers are only useful for detecting and diagnosing the absence of NSCLC, methods for grouping multiple subsets of the NSCLC biomarkers that are each useful as a panel of three or more biomarkers are also described herein. . Therefore, various embodiments of the present application provide combinations comprising N biomarkers, wherein N is at least three biomarkers. In other embodiments, N is selected to be any number of 2-59 biomarkers. It will be appreciated that N can be selected to be any number of any of the ranges described above, as well as similar, but higher order intervals. According to any of the methods described herein, biomarker values can be detected and classified individually or can be detected and classified collectively, as for example in a multiple assay format.

Classification of biomarkers and calculation of disease scores

A biomarker "badge" for a given diagnostic test contains a set of markers, with each marker having different levels in the populations of interest. Different levels, in this context, may refer to different means of marker levels for individuals in two or more groups, or different variances in the two or more groups, or a combination of both. For the simplest form of a diagnostic test, these markers can be used to assign an unknown sample of an individual to one of two groups, either sick or not sick. The assignment of a sample to one of two or more groups is known as a classification, and the procedure used to achieve this assignment is known as a classifier or a method of classification. Classification methods can also be called scoring methods. There are many classification methods that can be used to construct a diagnostic classifier of a set of biomarker values. In general, classification methods are more easily performed using supervised learning techniques where a set of data is collected using samples obtained from individuals in two (or more, for multiple classification states) different groups that you want to distinguish. Since the class (group or population) to which each sample belongs is previously known for each sample, the classification method can be trained to provide the desired classification response. It is also possible to use unsupervised learning techniques to produce a diagnostic classifier.

Common approaches to the development of diagnostic classifiers include decision trees; aggregation, reinforcement, forests and random forests; learning based on inference of norms; Parzen windows; linear models; Logistics; neural network methods; unsupervised grouping; K-stockings; hierarchical ascending / descending; semi-supervised learning; prototype methods; nearest neighbor; kernel density estimation; support vector machines; hidden Markov models; Boltzmann learning; and classifiers can be combined simply or in ways that minimize particular objective functions. For a review, see, for example, Pattern Classification, R. O. Duda, et al., Editors, John Wiley & Sons, 2nd edition, 2001; see also, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science + Business Media, LLC, 2nd edition, 2009; each of which is incorporated by reference in its entirety.

To produce a classifier using supervised learning techniques, a set of samples called training data is obtained. In the context of diagnostic tests, the treatment data includes samples from the defined groups (classes) to which unknown samples will be assigned later. For example, samples collected from individuals in a control population and individuals in a particular disease population may constitute training data to develop a classifier that can classify unknown samples (or, more particularly, the individuals from which the samples were obtained. ) as suffering from the disease or exempt from the disease. The development of the classifier from training data is known as classifier training. Specific details about training

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Classifiers depend on the nature of the supervised learning technique. For illustrative purposes, an example of training a simple Bayesian classifier will be described later (see, for example, Pattern Classification, RO Duda, et al., Editors, John Wiley & Sons, 2nd edition, 2001; see also, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., Editors, Springer Science + Business Media, LLC, 2nd edition, 2009).

Since there are usually many more potential biomarker values than samples in a training set, care must be taken to avoid overfitting. Overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Overfitting can be avoided in a variety of ways, including, for example, limiting the number of markers used in the development of the classifier, assuming that the marker responses are independent of each other, limiting the complexity of the underlying statistical model employed and ensuring that the statistical model underlying fits the data.

An illustrative example of the development of a diagnostic test using a set of biomarkers includes the application of a simple Bayesian classifier, a simple probabilistic classifier based on Bayes' theorem with strict independent biomarker treatment. Each biomarker is described by a class dependent probability density (fdp) function for measured UFR values or logarithmic UFR (relative fluorescence units) values in each class. It is assumed that the fdp linked to the set of markers in a class is the product of the individual class dependent fdp for each biomarker. Training a simple Bayesian classifier in this context is equivalent to assigning parameters ("parameterization") to characterize the class-dependent fdp. Any underlying model can be used for class-dependent fdp, but the model should generally conform to the data observed in the training set.

Specifically, the class-dependent probability of measuring an Xi value for the biomarker i in the disease class is written as p (x \ d) and the general simple Bayesian probability of observing n markers with

p (x \ d) - n? = i p (xíId)

values x = (x-i, X2, ... xn) is written as where the individual x, s are the levels

of biomarkers measured in UFR or UFR log. The classification assignment for a stranger is facilitated by calculating the probability of being sick p (d \ x) having measured x compared to the probability of being free of disease (control) p (c | jí) for the same measured values. The relationship of these probabilities is calculated from the class-dependent pfd by application of Bayes' theorem, that is, pd |.?) _ P (x \ d) p (d)

Pk’kH1-PÍ ^ .P1 where p (d) is the prevalence of the disease in the appropriate population for the trial. Taking the logarithm of both sides of this relationship and replacing the simple Bayesian class dependent probabilities of before you get

ln

image 1

n

í>

\ p (* Ti | c) and

+ ln

(Ask) \

\ l-p (d) J-

This form is known as the logarithmic probability ratio and simply indicates the logarithmic probability of being exempt from the particular disease versus having the disease and is mainly composed of the sum of individual logarithmic probability ratios of the n individual biomarkers. In its simplest form, an unknown sample (or, more particularly, the individual from whom the sample was obtained) is classified as exempt from the disease if the previous relationship is greater than zero and as afflicted with the disease if the relationship is less that zero

In an exemplary embodiment, it is assumed that the fdp of dependent biomarkers of class p (x ^ c) and p (x / [d) are normal or normal logarithmic distributions at the measured UFR values x, i.e.

image2

with a similar expression for p (x / [d) with pd and Od. The parameterization of the model requires the estimation of two parameters for each class-dependent fdp, a mean p and a variance a2, from the training data. This can be achieved in several ways, including, for example, by estimates of maximum probability, by least squares and by any other method known to those skilled in the art. The substitution of the normal distributions for p and a in the previous logarithmic probability ratio provides the following expression:

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image3

Once a set of ys and a2s has been defined for each fdp in each class of the training data and the prevalence of disease in the population has been specified, the Bayesian classifier is fully determined and can be used to classify unknown samples with values x measured.

The performance of the simple Bayesian classifier depends on the number and quality of the biomarkers used to build and train the classifier. A single biomarker will perform according to its distance KS (Kolmogorov-Smirnov), as defined in Example 3, below. If a performance measure of a classifier is defined as the area under the curve (ABC) of the receiving operator characteristic, a perfect classifier will have a score of 1 and a random classifier, on average, will have a score of 0.5. The definition of the distance KS between two sets A and B of sizes n and m is the value, Dn, m = supxFA, n (x) - Fb, m (x) |, which is the largest difference between two cumulative distribution functions ( fda) empirical. The empirical fda for a set A of n

i "

FU *) =

Remarks X¡ is defined as, `` where IxSx is the indicator function that is equal

a 1 if Xi <x y is otherwise equal to 0. By definition, this value is bounded between 0 and 1, where a distance KS of 1 indicates that the empirical distributions do not overlap.

The addition of subsequent markers with good KS distances (> 0.3, for example), in general, will improve the classification performance if the markers added subsequently are independent of the first marker. Using the area under the ROC curve (ABC) as a classifier score, it is easy to generate many high score classifiers with a variation of an avid algorithm. (An avid algorithm is any algorithm that follows the problem-solving metaheuristic of making the locally optimal decision at each stage in the hope of finding the global optimum.)

The algorithm approach used here is described in detail in Example 4. Briefly, all individual analyte classifiers are generated from a table of potential biomarkers and added to a list. Then, all possible additions of a second analyte are then made to each of the stored individual analyte classifiers, saving a predetermined number of the best score pairs, such as a thousand, in a new list. All classifiers of three possible markers are explored using this new list of the best classifiers of two markers, saving again the best one thousand of these. This process continues until the score reaches a stable level or begins to deteriorate as additional markers are added. High-ranking classifiers that remain after convergence can be evaluated with respect to the desired performance for an intended use. For example, in a diagnostic application, classifiers with high sensitivity and moderate specificity may be more desirable than moderate sensitivity and high specificity. In another diagnostic application, classifiers with high specificity and modest sensitivity may be more desirable. The desired level of performance is generally selected based on a counterpart to be given between the number of false positives and false negatives that can each be tolerated for the particular diagnostic application. Such counterparts generally depend on the medical consequences of an error, either false positive or false negative.

Various other techniques are known in the art and can be used to generate many potential classifiers from a biomarker list using a simple Bayesian classifier. In one embodiment, what is called a genetic algorithm can be used to combine different markers using the adjustment score as defined above. Genetic algorithms fit particularly well to the exploration of a large diverse population of potential classifiers. In another embodiment, the so-called ant colony optimization can be used to generate sets of classifiers. Other strategies known in the art may also be employed, including, for example, other evolutionary strategies as well as simulated hybridization and other stochastic search methods. Metaheuristic methods such as, for example, search for harmony can also be used.

Exemplary embodiments use any number of the NSCLC biomarkers listed in Table 1 in various combinations to produce diagnostic assays to detect NSCLC (see Example 2 for a detailed description of how these biomarkers were identified). In one embodiment, a method of diagnosis of NSCLC uses a simple Bayesian classification method together with any of several CPNM biomarkers listed in Table 1. In an illustrative example (Example 3), the simplest test for detecting NSCLC of a population of smokers and benign lung nodules can be constructed using a single biomarker, for example, MMP7 that is differentially expressed in NSCLC with a KS distance of 0.59. Using the parameters, Uc, ¡, ac i, uu and adj for mMP7 of Table 16 and the equation for the logarithmic probability

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described above, a diagnostic test with an ABC of 0.803 can be obtained, see Table 15. The ROC curve for this test is presented in Figure 2.

The addition of the CLIC1 biomarker, for example, with a KS distance of 0.53, significantly improves the performance of the classifier at an ABC of 0.883. Note that the score for a classifier constructed by two biomarkers is not a simple sum of the KS distances; KS distances are not additive when biomarkers are combined and many more weak markers are necessary to achieve the same level of performance as a strong marker. The addition of a third marker, STX1A, for example, reinforces the performance of the classifier at an ABC of 0.901. The addition of additional biomarkers, such as, for example, CHRDL1, PA2G4, SERPINA1, BDNF, GHR, TGFBI and NME2, produces a series of NSCLC tests summarized in Table 15 and presented as a series of ROC curves in Figure 3. The score of the classifiers based on the number of analytes used in the classifier construction is presented in Figure 4. The ABC of this classifier of ten markers by way of example is 0.948.

The markers listed in Table 1 can be combined in many ways to produce classifiers to diagnose NSCLC. In some embodiments, biomarker panels are comprised of different numbers of analytes depending on a specific diagnostic performance criteria that is selected. For example, certain combinations of biomarkers will produce assays that are more sensitive (or more specific) than other combinations.

Once a panel has been defined to include a particular set of biomarkers from Table 1 and a classifier has been constructed from a set of training data, the definition of the diagnostic test is complete. In one embodiment, the procedure used to classify an unknown sample is outlined in Figure 1A. In another embodiment the procedure used to classify an unknown sample is outlined in Figure 1B. The biological sample is diluted appropriately and then processed in one or more assays to produce the levels of relevant quantitative biomarkers used for classification. The measured biomarker levels are used as input for the classification method that produces a classification and an optional score for the sample that reflects the confidence of the class assignment.

Table 1 identifies 59 biomarkers that are useful for diagnosing NSCLC. This is a surprisingly higher than expected number compared to what is normally found during attempts to discover biomarkers and may be attributable to the scale of the study described, which covered more than 1000 proteins measured in hundreds of individual samples, in some cases at concentrations in the low femtomolar interval. Supposedly, the large number of biomarkers discovered reflects the various biochemical pathways involved both in tumor biology and in the body's response to the presence of the tumor; Each route and process involves many proteins. The results show that no single protein in a small group of proteins is uniquely informative about such complex processes; rather, multiple proteins are involved in relevant processes, such as apoptosis or extracellular matrix repair, for example.

Given the numerous biomarkers identified during the study described, one would expect to be able to obtain large numbers of high performance classifiers that can be used in various diagnostic methods. To verify this idea, tens of thousands of classifiers were evaluated using the biomarkers in Table 1. As described in Example 4, many subsets of the biomarkers presented in Table 1 can be combined to generate useful classifiers. By way of example, descriptions are provided for classifiers containing 1, 2 and 3 biomarkers for NSCLC detection. As described in Example 4, all classifiers that were constructed using the biomarkers in Table 1 performed remarkably better than classifiers that were constructed using "non-markers."

The performance of classifiers obtained by randomly excluding some of the markers in Table 1, which resulted in smaller subsets from which to build the classifiers, was also tested. As described in Example 4, the classifiers that were constructed from random subsets of the markers in Table 1 yielded similarly to optimal classifiers that were constructed using the complete list of markers in Table 1.

The performance of classifiers of ten markers obtained by excluding the "best" individual markers from the aggregation of ten markers was also tested. As described in Example 4, the classifiers constructed with the "best" markers in Table 1 also performed well. Many subsets of the biomarkers listed in Table 1 performed almost optimally, even after removing the top 15 of the markers listed in the Table. This implies that the performance characteristics of any particular classifier are probably not due to a small central group of biomarkers and that the disease process probably influences numerous biochemical pathways, which alters the level of expression of many proteins.

The results of Example 4 suggest some possible conclusions: First, the identification of a large number of biomarkers allows their aggregation in a large number of classifiers that offer a yield

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high similar. Second, classifiers can be constructed so that particular biomarkers can be substituted for other biomarkers in a way that reflects the redundancies that undoubtedly exist in the complexities of the underlying disease processes. That is, information about the disease contributed by any individual biomarker identified in Table 1 overlaps with the information provided by other biomarkers, so that it may happen that no particular biomarker or small group of biomarkers should be included in Table 1 in no classifier

Exemplary embodiments use simple Bayesian classifiers constructed from the data in Table 16 to classify an unknown sample. The procedure is outlined in Figures 1A and 1B. In one embodiment, the biological sample is optionally diluted and processed in a multiple aptamer assay. The test data is normalized and calibrated as outlined in Example 3 and the resulting biomarker levels are used as input in a Bayesian classification scheme. The logarithmic probability ratio is calculated for each biomarker measured individually and then added to produce a final classification score, which is also called a diagnostic score. The resulting assignment as well as the general classification score can be indicated. Optionally, the individual logarithmic probability risk factors calculated for each biomarker level can also be indicated. The details of the calculation of the classification score are presented in Example 3.

Kits

Any combination of the biomarkers in Table 1 (as well as additional biomedical information) can be detected using a suitable kit, such as for use in performing the methods disclosed herein. In addition, any kit may contain one or more detectable markers as described herein, such as a fluorescent moiety, etc.

In one embodiment, a kit includes (a) one or more capture reagents (such as, for example, at least one aptamer or antibody) to detect one or more biomarkers in a biological sample, wherein the biomarkers include any of the biomarkers. set forth in Table 1, and optionally (b) one or more software or software products to classify the individual from whom the biological sample was obtained as having or not suffering from NSCLC or to determine the probability that the individual has NSCLC, as described further herein. Alternatively, instead of one or more software products, one or more instructions may be provided for a human being to manually perform the above steps.

The combination of a solid support with a corresponding capture reagent and a signal generating material is referred to herein as "detection device" or "kit." The kit may also include instructions for using the devices and reagents, manipulating the sample and analyzing the data. In addition, the kit can be used with a computer system or software to analyze and present the result of the biological sample analysis.

The kits may also contain one or more reagents (for example, solubilization buffers, detergents, washes or buffers) to process a biological sample. Any of the kits described herein may also include, for example, buffers, blocking agents, mass spectrometry matrix materials, antibody capture agents, positive control samples, negative control samples, software and information such as protocols, guidance and reference data.

In one aspect, the invention provides kits for the analysis of the status of NSCLC. The kits include PCR primers for one or more biomarkers selected from Table 1. The kit may also include instructions for use and correlation of biomarkers with NSCLC. The kit may also include a DNA matrix containing the complement of one or more of the biomarkers selected from Table 1, reagents and / or enzymes to amplify or isolate sample DNA. The kits may include real-time PCR reagents, for example, TaqMan probes and / or primers and enzymes.

For example, a kit may comprise (a) reagents comprising at least capture reagent to quantify one or more biomarkers in a test sample, wherein said biomarkers comprise the biomarkers set forth in Table 1, any other biomarker or biomarker panels. described herein, and optionally (b) one or more algorithms or computer programs to perform the steps of comparing the amount of each quantized biomarker in the test sample with one or more predetermined cut-off points and assign a score for each biomarker quantified based on said comparison, combine the assigned scores for each quantified biomarker to obtain a total score, compare the total score with a predetermined score and use that comparison to determine if an individual has NSCLC. Alternatively, instead of one or more algorithms or computer programs, one or more instructions may be provided for a human being to perform the above steps manually.

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Computer methods and software

Once a biomarker or panel of biomarkers has been selected, a method of diagnosing an individual may comprise the following: 1) collecting or otherwise obtaining a biological sample; 2) perform an analytical method to detect and measure the biomarker or biomarkers on the panel in the biological sample; 3) perform any standardization or unification of data required for the method used to collect biomarker values; 4) calculate the marker score; 5) combine marker scores to obtain a total diagnostic score; and 6) present the individual's diagnostic score. In this approach, the diagnostic score can be a single number determined from the sum of all marker calculations that is compared with a predetermined threshold value that is an indication of the presence or absence of disease. Or the diagnostic score can be a series of bars that each represent a biomarker value and the pattern of the responses can be compared with a predetermined pattern for the determination of the presence or absence of disease.

At least some embodiments of the methods described herein can be implemented with the use of a computer. An example of a computer system 100 is shown in Figure 6. With reference to Figure 6, the system 100 is shown comprising hardware elements that are electrically coupled via bus 108, including a processor 101, input device 102, device output 103, storage device 104, computer readable storage media reader 105a, communications system 106 that processes acceleration (eg, DSP specialized processors) 107 and memory 109. The computer readable storage media reader 105a it is additionally coupled to computer readable storage medium 105b, the combination of remote, local, fixed and / or removable storage devices plus storage medium, memory, etc. representing exhaustively. to temporarily and / or more permanently contain computer readable information, which may include storage device 104, memory 109 and / or any other such accessible system resources 100. System 100 also comprises software elements (shown as currently located in working memory 191) including an operating system 192 and other code 193, such as programs, data and the like.

With respect to Figure 6, the system 100 has extensive flexibility and configurability. Therefore, for example, a single architecture could be used to implement one or more servers that can be additionally configured according to currently desirable protocols, protocol variations, extensions, etc. However, it will be apparent to those skilled in the art that embodiments can be used in accordance with more specific application requirements. For example, one or more system elements could be implemented as sub-elements in a system component 100 (for example, in communications system 106). Adapted hardware could also be used and / or particular elements could be implemented in hardware, software or both. In addition, although connection with other computing devices such as network input / output devices (not shown) can be used, it should be understood that connection or connections by cable, without cable, by modem and / or others with other computing devices could also be used.

In one aspect, the system may comprise a database that contains elements of biomarkers characteristic of NSCLC. Biomarker data (or biomarker information) can be used as an entry in the computer for use as part of a computer-implemented method. The biomarker data may include the data as described herein.

In one aspect, the system further comprises one or more devices to provide input data to one or more processors.

The system further comprises a memory for storing a data set of classified data elements.

In another aspect, the device for providing input data comprises a detector for detecting the characteristic of the data element, for example, such as a mass spectrometer or gene chip reader.

The system may additionally comprise a database control system. User requests or queries can be formatted in an appropriate language understood by the database control system that processes the query to extract the relevant information from the training set database.

The system can be connectable to a network with which a network server and one or more clients are connected. The network can be a local area network (LAN) or a wide area network (WAN), as is known in the art. Preferably, the server includes the hardware necessary to run computer program products (eg, software) to access database data to process user requests.

The system may include an operating system (for example, UNIX or Linux) to execute instructions from a database control system. In one aspect, the operating system can act on a global communications network, such as the Internet, and use a global communications network server to connect to said network.

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The system may include one or more devices comprising a graphic display interface comprising interface elements such as buttons, drop-down menus, scroll bars, fields for entering text and the like as routinely found in the graphical user interfaces known in the technique. Requests entered in a user interface can be transmitted to an application program in the system for formatting to search for relevant information in one or more of the system databases. Requests or queries entered by a user can be constructed in any suitable database language.

The graphical user interface can be generated by a graphical user interface code as part of the operating system and can be used to enter data and / or present entered data. The result of processed data can be presented on the interface, printed on a printer in communication with the system, stored in a memory device and / or transmitted over the network or can be provided in the form of the computer readable medium.

The system may be in communication with an input device to provide data regarding data elements to the system (eg, expression values). In one aspect, the input device may include a gene expression profile system that includes, for example, a mass spectrometer, gene chip reader or matrices, and the like.

The methods and apparatus for analyzing information of NSCLC biomarkers according to various embodiments can be implemented in any suitable manner, for example, using a computer program that operates in a computer system. A conventional computer system comprising a processor and a direct access memory may be used, such as a remotely accessible application server, network server, personal computer or workstation. Additional computer system components may include memory devices or information storage systems, such as a mass storage system and a user interface, for example a conventional monitor, keyboard and tracking device. The computer system may be an independent system or part of a computer network that includes a server and one or more databases.

The CPNM biomarker analysis system can provide functions and operations to complete data analysis, such as data collection, processing, analysis, presentation and / or diagnosis. For example, in one embodiment, the computer system can execute the computer program that can receive, store, search, analyze and present information related to the CPNM biomarkers. The computer program may comprise multiple modules that perform various functions or operations, such as a processing module to process raw data and generate complementary data and an analysis module to analyze raw data and complementary data to generate a status and / or diagnosis of NSCLC. The diagnosis of NSCLC status may include generating or collecting any other information, including additional biomedical information, regarding the condition of the individual in relation to the disease, identifying whether additional trials may be desirable or otherwise evaluating the health status of the individual.

Referring now to Figure 7, an example of a method for using a computer according to principles of a disclosed embodiment can be seen. In Figure 7, a flow chart 3000 is shown. In block 3004, biomarker information can be retrieved for an individual. Biomarker information can be retrieved from a computer database, for example, after testing the individual's biological sample. The biomarker information may comprise biomarker values each corresponding to one of at least N biomarkers selected from a group consisting of the biomarkers provided in Table 1, where n = 2-59. In block 3008, a computer can be used to classify each of the biomarker values. And, in block 3012, a determination can be made regarding the probability that an individual has NSCLC based on a plurality of classifications. The indication can be extracted to a monitor or other indicating device so that it is visible by a person. Therefore, for example, it can be presented on a screen of a computer or other output device.

Referring now to Figure 8, an alternative method for using a computer according to another embodiment can be illustrated by flowchart 3200. In block 3204, a computer can be used to retrieve biomarker information for an individual. The biomarker information comprises a biomarker value corresponding to a biomarker selected from the group of biomarkers provided in Table 1. In block 3208, a classification of the biomarker value can be made with the computer. And, in block 3212, an indication can be made regarding the probability that an individual has NSCLC based on the classification. The indication can be extracted to a monitor or other indicating device so that it is visible by a person. Therefore, for example, it can be presented on a screen of a computer or other output device.

Some embodiments described herein may be implemented to include a computer program product. A computer program product may include a computer readable medium that has a computer readable program code incorporated in the medium to cause an application program to run on a computer with a database.

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As used herein, a "computer program product" refers to an organized set of instructions in the form of instructions in natural language or programming that are contained in a physical medium of any nature (eg, written, electronic , magnetic, optical or other) and can be used with a computer or other automatic data processing system. Said instructions in programming language, when executed by a computer or data processing system, cause the computer or data processing system to act in accordance with the particular content of the instructions. The products of computer programs include without limitation: programs in source and object libraries and / or essay or data included in a computer readable medium. In addition, the computer program product can be provided that allows a computer system or data processing equipment device to act in preselected modes in various ways, including, but not limited to, original source code, assembly code, object code, machine language , encrypted or compressed versions of the foregoing and each and every equivalent.

In one aspect, a computer program product is provided to indicate a probability of NSCLC. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise biomarker values that each correspond to one of at least N biomarkers in the biological sample selected from the biomarker group provided in Table 1, where n = 2-59; and code that executes a classification method that indicates an individual's NSCLC status based on biomarker values.

In yet another aspect, a computer program product is provided to indicate a probability of NSCLC. The computer program product includes a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data they comprise a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers provided in Table 1; and code that executes a classification method that indicates an individual's NSCLC status based on the biomarker value.

Although various embodiments have been described as methods or apparatus, it should be understood that embodiments can be implemented by code together with a computer, for example, code resident in a computer or accessible to the computer. For example, software and databases could be used to implement many of the methods discussed above. Therefore, in addition to hardware-made embodiments, it should also be noted that these embodiments can be made by using a manufacturing article comprised in a computer readable medium that has a computer readable program code incorporated therein, which causes the enablement of the functions disclosed in this description. Therefore, it is desired that the embodiments also be considered protected by this patent in its program code medium. In addition, the embodiments can be incorporated as code stored in a computer readable memory of virtually any type including, without limitation, RAM, ROM, magnetic media, optical media or magneto-optical media. Even more generally, the embodiments could be implemented in software, or in hardware, or any combination thereof including, but not limited to, software running on a general purpose processor, microcode, PLA or ASIC.

It is also envisioned that embodiments could be made as computer signals incorporated in a carrier wave, as well as signals (eg, electrical and optical) propagated through a transmission medium. Therefore, the various types of information discussed above could be formatted in a structure, such as a data structure, and transmitted as an electrical signal through a transmission medium or stored in a computer readable medium.

It should also be noted that many of the structures, materials and acts listed in this document can be listed as a means to perform a function or a stage to perform a function. Therefore, it should be understood that such language has the right to cover all those structures, materials or acts disclosed in this specification and its equivalents, including the subject incorporated by reference.

The biomarker identification process, the use of the biomarkers disclosed herein and the various methods for determining biomarker values have been described in detail above with respect to NSCLC. However, the application of the process, the use of identified biomarkers and the methods for determining biomarker values are fully applicable to other specific types of cancer, to cancer in general, to any other medical disease or condition or to the identification of individuals who may Benefit or not from a complementary medical treatment. Except when referring to specific results related to NSCLC, as is evident from the context, it can be understood that references in this document to NSCLC include other types of cancer, cancer in general or any other disease or medical condition.

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Examples

The following examples are provided for illustrative purposes only and are not intended to limit the scope of the patent as defined by the appended claims. All examples described herein were carried out using conventional techniques, which are well known and routine for those skilled in the art. The routine molecular biology techniques described in the following examples can be carried out as described in conventional laboratory manuals, such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (2001).

Example 1. Analysis of multiple sample aptamers

This example describes the multiple aptamer assay used to analyze the samples and controls for the identification of the biomarkers set forth in Table 1 (see Figure 9) and the identification of the cancer biomarkers set forth in Table 19. For studies of NSCLC, mesothelioma and renal cell carcinoma, multiple analysis used 1,034 aptamers, each unique to a specific target.

In this method, the pipette tips were changed for each solution addition.

In addition, unless otherwise indicated, most solution transfers and wash additions used the 96-well head of a Beckman Biomek FxP. The manually pipetted method steps used a twelve-channel P200 Pipetteman (Rainin Instruments, LLC, Oakland, CA), unless otherwise indicated. An adapted buffer called SB17 was prepared internally, comprising 40 mM HEPES, 100 mM NaCl, 5 mM KCl, 5 mM MgCh, 1 mM EDTA at pH 7.5. An adapted buffer called SB18 was prepared internally, comprising 40 mM HEPES, 100 mM NaCl, 5 mM Kcl, 5 mM MgC ^ pH 7.5. All stages were performed at room temperature unless otherwise indicated.

1. Preparation of aptamer stock solution

Solutions of reserve aptamers adapted for 5%, 0.316% and 0.01% serum at 2x concentration in 1x SB17, 0.05% Tween-20 were prepared.

These solutions are stored at -20 ° C until use. On the day of the test, each mixture of aptamers was thawed at 37 ° C for 10 minutes, placed in a boiling water bath for 10 minutes and allowed to cool to 25 ° C for 20 minutes with vigorous mixing between each stage of heating. After heating-cooling, 55 pl of each mixture of 2x aptamers were manually pipetted into a 96-well Hybaid plate and the plate was sealed with aluminum foil. The final result was three Hybaid plates sealed with 96-well foil, with mixtures of aptamers of 5%, 0.316% or 0.01%. The concentration of individual aptamers was 2x final or 1 nM.

2. Preparation of test samples

Frozen aliquots of 100% serum or plasma were placed, stored at -80 ° C in a 25 ° C water bath for 10 minutes. Defrosted samples were placed on ice, vortexed gently (adjusted to 4) for 8 seconds and then repositioned on ice.

A 10% sample solution (final 2x) was prepared by transferring 8 pl of sample using a pipettor covering 8 channels of 50 pl in 96-well Hybaid plates, each well containing 72 pl of the appropriate sample diluent at 4 ° C (SB17 1x for serum or SB18 0.8x for plasma, plus Tween-20 0.06%, Z-block_2 11.1 pM, MgCl2 0.44 mM, AEBSF 2.2 mM, EgTA 1.1 mM, EDTA 55 , 6 pM). This plate was stored on ice until the following sample dilution stages were initiated in the BiomekFxP robot.

To begin balancing samples and aptamers, the 10% sample plate was centrifuged briefly and placed in the Beckman FX where it was mixed by pipetting up and down with the 96-well pipettor. A 0.632% sample plate (2x final) was then prepared by diluting 6 pl of the 10% sample in 89 pl of SB17 1x Tween-20 0.05% with 2 mM AEBSF. Next, the 6 pl dilution of the resulting 0.632% sample in 184 pl of 1x SB17, Tween-20 0.05% made a 0.02% sample plate (final 2x). Dilutions were made in the Beckman Biomek FxP. After each transfer, the solutions were mixed by pipetting up and down. The 3 sample dilution plates were then transferred to their respective aptamer solutions by adding 55 pl of the sample to 55 pl of the 2x aptamer mixture. Sample solutions and aptamers were mixed in the robot by pipetting up and down.

3. Sample Balancing Union

Sample plates / aptamers were sealed with aluminum foil and placed in an incubator at 37 ° C for 3.5 hours before continuing to the Capture 1 stage.

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4. Preparation of Capture 2 Pearl Plate

An 11 ml aliquot of Streptavidin C1 MyOne beads (Invitrogen Corp., Carlsbad, CA) (10 mg / ml) was washed twice with equal volumes of 20 mM NaOH (5 minute incubation for each wash), 3 times with equal volumes of 1x SB17, 0.05% Tween-20 and resuspended in 11 ml of 1x SB17, 0.05% Tween-20. Using a 12-channel multichannel pipettor, 50 pl of this solution was manually pipetted into each well of a 96-well Hybaid plate. The plate was then covered with aluminum foil and stored at 4 ° C for use in the assay.

5. Preparation of pearl plate Capture 1

Three 0.45 pm Millipore HV plates (Durapore membrane, Cat # MAHVN4550) were equilibrated with 100 µL of 1x SB17, 0.05% Tween-20 for at least 10 minutes. The equilibration buffer was then filtered through the plate and 133.3 pl of a 7.5% streptavidin-agarose bead suspension (in 1x SB17, 0.05% Tween-20) was added to each well. To keep streptavidin-agarose beads suspended while transferring to the filter plate, the pearl solution was mixed manually with a 200-channel 12-channel pipettor, at least 6 times between pipetting events. After the beads were distributed through the 3 filter plates, a vacuum was applied to remove the pearl supernatant. Finally, the beads were washed on the filter plates with 200 µl of 1x SB17, 0.05% Tween-20 and then resuspended in 200 µl of 1x SB17, 0.05% Tween-20. The bottoms of the filter plates were transferred and the plates were stored for use in the assay.

6. Cytomat charge

The cytomat was loaded with all tips, plates, all reagents in channels (except NHS-biotin reagent that was re-prepared just before the addition to the plates), 3 Capture 1 filter plates prepared and 1 MyOne plate ready

7. Capture 1

After a balancing time of 3.5 hours, the sample / aptamer plates were removed from the incubator, centrifuged for approximately 1 minute, the cover was removed and placed on the Beckman Biomek FxP platform. The Beckman Biomek FxP program began. All subsequent stages in Capture 1 were performed by the Beckman Biomek FxP robot unless otherwise indicated. Within the program, the vacuum was applied to the Capture 1 filter plates to remove the pearl supernatant. One hundred microliters of each of the 5%, 0.316% and 0.01% equilibrium binding reactions were added to their respective Capture 1 filtration plates, and each plate was mixed using a platform orbital shaker at 800 rpm for 10 minutes

Unbound solution was removed by vacuum filtration. Capture 1 beads were washed with 190 µl of 100 pM biotin in 1x SB17, 0.05% Tween-20 followed by 5x 190 pl of 1x SB17, 0.05% Tween-20 distributing the solution and immediately removing a vacuum for Filter the solution through the plate.

8. Marking

An aliquot of 100 mM NHS-PEO4-biotin in anhydrous DMSO was thawed, thawed at 37 ° C for 6 minutes and then diluted 1: 100 with labeling buffer (SB17 at pH 7.25, Tween-20 0.05% ). After a robot boost, the diluted NHS-PEO4-biotin reagent was manually added to a platform groove and the robot program was manually restarted to distribute 100 pl of the NHS-PEO4-biotin into each well of each filter plate Capture 1. This solution was allowed to incubate with Capture 1 beads by stirring at 800 rpm for 5 minutes on the orbital shakers.

9. Kinetic exposure and photocision

The labeling reaction was removed by vacuum filtration and stopped by the addition of 150 μl of 20 mM glycine in 1x SB17, 0.05% Tween-20 to the Capture 1 plates. The NHS / glycine marker solution was removed by vacuum filtration. Next, 1500 µl of 20 mM glycine (1x SB17, 0.05% Tween-20) was added to each plate and incubated for 1 minute in orbital shakers at 800 rpm before vacuum filtration removal.

The wells of Capture 1 plates were subsequently washed three times by adding 190 pl of 1x SB17, 0.05% Tween-20, followed by vacuum filtration and then adding 190 pl of 1x SB17, 0.05% Tween-20 with stirring for 1 minute at 800 rpm followed by vacuum filtration. After the last wash the plates were placed on a 1 ml deep well plate and removed from the platform. Capture 1 plates were centrifuged at 1000 rpm for 1 minute to remove as much superfluous volume from the agarose beads before elution as possible.

The plates were placed back in the Beckman Biomek FxP and 85 pl of 10 mM DxSO4 in 1x SB17, 0.05% Tween-20 was added to each well of the filter plates.

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The filter plates were removed from the platform, placed in a Variomag thermo-agitator (Thermo Fisher Scientific, Inc., Waltham, MA) under BlackRay light sources (Ted Pella, Inc., Redding, CA) and irradiated for 5 minutes stirring at 800 rpm. After incubation for 5 minutes the plates were rotated 180 degrees and irradiated with stirring for an additional 5 minutes.

Photo cleaved solutions were sequentially eluted from each Capture 1 plate to a common deep well plate by first placing the 5% Capture filter plate 1 on a 1 ml deep well plate and centrifuging at 1000 rpm for 1 minute. Capture 1 0.316% and 0.01% plates were then centrifuged sequentially in the same deep well plate.

10. Pearl Capture Capture 2

The 1 ml deep well block containing the combined eluates from Captura 1 was placed on the Beckman Biomek FxP platform for Captura 2.

The robot transferred all of the photoescised eluate from the 1 ml deep well plate into the Hybaid plate containing the previously prepared MyOne Capture 2 magnetic beads (after removal of the MyOne buffer by magnetic separation).

The solution was incubated while stirring at 1350 rpm for 5 minutes at 25 ° C in a Variomag thermo-agitator (Thermo Fisher Scientific, Inc., Waltham, MA).

The robot transferred the plate to the magnetic separator station on the platform. The plate was incubated on the magnet for 90 seconds before removal and discarding the supernatant.

30% glycerol washes at 11.37 ° C

Capture 2 plate was moved to the platform thermal agitator and 75 pl of 1x SB17, 0.05% Tween-20 was transferred to each well. The plate was mixed for 1 minute at 1350 rpm and 37 ° C to resuspend and heat the beads. To each well of the Capture 2 plate, 75 µl of 60% glycerol was transferred at 37 ° C and the plate continued to be mixed for another minute at 1350 rpm and 37 ° C. The robot transferred the plate to the magnetic separator at 37 ° C where it was incubated in the magnet for 2 minutes and then the robot removed and discarded the supernatant. These washes were repeated twice more.

After removal of the third 30% glycerol wash from Captura 2 beads, 150 µL of 1x SB17, 0.05% Tween-20 was added to each well and incubated at 37 ° C, stirring at 1350 rpm for 1 minute , before removal by magnetic separation in the magnet at 37 ° C.

Capture 2 beads were washed once final using 150 µl of 1x SB17, 0.05% Tween-20 with incubation for 1 minute while stirring at 1350 rpm at 25 ° C before magnetic separation.

12. Elution and neutralization of Capture 2 beads

The aptamers were eluted from Capture 2 beads by adding 105 µl of 100 mM CAPSO with 1 M NaCl, 0.05% Tween-20 to each well. The beads were incubated with this solution with stirring at 1300 rpm for 5 minutes.

The Capture 2 plate was then placed in the magnetic separator for 90 seconds before transferring 63 pl of the eluate to a new 96-well plate containing 7 pl of 500 mM HCl, 500 mM HEPES, 0.05% Tween-20 in each well After transfer, the solution was mixed robotically by pipetting 60 pl up and down five times.

13. Hybridization

The Beckman Biomek FxP transferred 20 pl of the neutralized Capture 2 eluate to a new Hybaid plate, and 6 pl of 10x Agilent Block, containing a 10x addition of hybridization controls, was added to each well. Then, 30 pl of Agilent 2x hybridization buffer was pipetted manually to each well of the plate containing the neutralized samples and blocking buffer and the solution was mixed by manually pipetting 25 pl up and down 15 times slowly to avoid bubble formation extensive. The plate was centrifuged at 1000 rpm for 1 minute.

Adapted Agilent microarray slides (Agilent Technologies, Inc., Santa Clara, CA) were designed to contain complementary probes of the aptamer random region plus some primer region. For most aptamers, the optimal length of the complementary sequence was determined empirically and varied between 40-50 nucleotides. For subsequent aptamers, a complementary region of 46 units was chosen by default. The probes were attached to the slide surface with a poly T linker for a total probe length of 60 nucleotides.

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A joint slide was placed in an Agilent hybridization chamber and 40 µl of each sample containing hybridization solution and manually blocked were pipetted into each joint. An 8-channel variable range pipettor was used in a manner that is intended to minimize bubble formation. Slides of adapted Agilent microarrays (Agilent Technologies, Inc., Santa Clara, CA) were slowly lowered, with their numerical barcode up, on the joint slides (see the Agilent manual for a detailed description).

The upper part of the hybridization chambers was placed on the slide / reinforcement sandwich and support brackets were placed on the complete assembly. These sets fastened tightly by turning the screws firmly.

Each slide / booster slide sandwich was visually inspected to ensure that the solution bubbles could move freely within the sample. If the bubble did not move freely the hybridization chamber assembly was gently tapped to release bubbles lodged near the joint.

The assembled hybridization chambers were incubated in an Agilent hybridization oven for 19 hours at 60 ° C rotating at 20 rpm.

14. Post-hybridization wash

Approximately 400 ml of Agilent 1 wash buffer was placed in each of two separate glass staining plates. One of the staining plates was placed on a magnetic stir plate and a slide rack and stir bar were placed in the buffer.

An Agilent 2 wash staining plate was prepared by placing a stir bar on an empty glass staining plate.

A fourth glass staining plate was removed for the final acetonitrile wash.

Each of the six hybridization chambers was dismantled. One by one, the slide / reinforcement sandwich was removed from its hybridization chamber and immersed in the staining plate containing Wash 1. The slide / reinforcement sandwich was opened using a pair of tweezers, still submerging the microarray slide. . The slide was quickly transferred to the slide rack on the wash stain plate 1 on the magnetic stir plate.

The slide rack rose and lowered gently 5 times. The magnetic stirrer was activated at a low setting and the slides were incubated for 5 minutes.

With one minute left for Wash 1, wash buffer 2 preheated to 37 ° C in an incubator was added to the second prepared staining plate. The slide rack was quickly transferred to wash buffer 2 and any excess buffer was removed at the bottom of the rack by scraping it on top of the staining plate. The slide rack rose and lowered gently 5 times. The magnetic stirrer was activated at a low setting and the slides were incubated for 5 minutes.

The slide rack was slowly removed from Wash 2, taking approximately 15 seconds to remove the slides from the solution.

With one minute remaining in Wash 2, acetonitrile (ACN) was added to the fourth staining plate. The slide rack was transferred to the acetonitrile staining plate. The slide rack rose and lowered gently 5 times. The magnetic stirrer was activated at a low setting and the slides were incubated for 5 minutes.

The slide rack was slowly removed from the ACN staining plate and placed on an absorbent towel. The bottom edges of the slides dried quickly and the slide was placed in a clean slide box.

15. Capturing microarray images

The microarray slides were placed in Agilent scanner slide containers and loaded into the Agilent microarray scanner according to the manufacturer's instructions.

Images of the slides were captured on the Cy3 channel at a resolution of 5 jm with the 100% PMT setting and the XRD option enabled at 0.05. The resulting tiff images were processed using Agilent version 10.5 element extraction software.

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Example 2. Identification of biomarkers

The identification of potential NSCLC biomarkers was performed for diagnosis of NSCLC in individuals with undetermined pulmonary nodules identified with a CT scan or other image capture method, high-risk smoking scan for NSCLC and diagnosis of an individual with NSCLC. The admission criteria for this study were smokers, 18 years of age or older, able to provide informed consent and blood sample and documented diagnosis of NSCLC or benign findings. For some cases, blood samples were collected before treatment or surgery and then diagnosed with NSCLC. The exclusion criteria included diagnosis or previous treatment of cancer (excluding squamous cell carcinoma of the skin) in a period of 5 years before blood collection. Serum samples were collected from 3 different sites and included 46 NSCLC samples and 218 control group samples as described in Table 17. The multiple aptamer affinity assay as described in Example 1 was used to measure and present UFR value for 1,034 analytes in each of these 264 samples.

Each of the case and control populations was compared separately generating cumulative class-dependent distribution functions (fda) for each of the 1034 analytes. The distance KS (Kolmogorov-Smirnov statistic) between values of two sets of samples is a non-parametric measurement of the extent to which the empirical distribution of the values of a set (Set A) differs from the distribution of values of the other set (Set B). For any value of a threshold T some proportion of the values of the set A will be less than T, and some proportion of the values of the set B will be less than T. The distance KS measures the maximum (unsigned) difference between the proportion of the values of the two sets for any chosen T.

This set of potential biomarkers can be used to construct classifiers that assign samples to a control or disease group. In fact, many of these classifiers were produced from these sets of biomarkers and the frequency with which any biomarker was used in good score classifiers was determined. The biomarkers that appeared most frequently among the highest scoring classifiers were the most useful for creating a diagnostic trial. In this example, Bayesian classifiers were used to explore the classification space but many other supervised learning techniques can be used for this purpose. The scoring adjustment of any individual classifier was calibrated by the area under the receiver operating characteristic curve (ABC of the ROC) of the classifier on the Bayesian surface assuming a disease prevalence of 0.5. This scoring metric varies from zero to one, one being a classifier without errors. The details of the construction of a Bayesian classifier from biomarker population measurements are described in the

Example 3

Using the 59 analytes in Table 1, a total of 964 classifiers of 10 analytes were discovered with an ABC of 0.94 to diagnose NSCLC of the control group. From this set of classifiers, it was discovered that a total of 12 biomarkers were present in 30% or more of the high score classifiers. Table 13 provides a list of these potential biomarkers and Figure 10 is a frequency representation for the identified biomarkers.

Example 3. Simple Bayesian classification for NSCLC

From the list of biomarkers identified as useful for differentiating between NSCLC and controls, a panel of ten biomarkers was selected and a simple Bayesian classifier was constructed, see Tables 16 and 18. The class-dependent probability density (fdp) functions , p (xjc) and p (x ^ d), where Xi is the log of the value of UFR measured for biomarker i, and c and d refer to control and disease populations, were modeled as log-normal distribution functions characterized by a mediate and variance a2 The biomarkers for fdp of the ten biomarkers are listed in Table 16 and an example of the raw data is presented along with the model adjusted to a normal fdp in Figure 5. The underlying assumption seems to fit the data quite well as shown in Figure 5.

The simple Bayesian classification for this model is provided by the following equation, where p (d) is the prevalence of the disease in the population,

ln

image4

n

X> i

1 = 1

image5

image6

image7

+ ln

(P (d) \

\ 1 - p (d) J

appropriate for the test and n = 10. Each of the terms in the sum is a logarithmic probability ratio for an individual marker and the total logarithmic probability ratio of a sample x that is exempt from the disease of interest (that is, in this case, NSCLC) versus one that has the disease is simply the sum of these individual terms plus a term that accounts for the prevalence of the disease.

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For simplicity, it is assumed that p (d) = 0.5 so that

image8

given an unknown sample measurement in / og (UFR) for each of the ten biomarkers of 6.9, 8.7, 7.9, 9.8, 8.4, 10.6, 7.3, 6, 3, 7.3, 8.1, the calculation of the classification is detailed in Table 16. The individual components that comprise the logarithmic probability relationship for disease class versus control are tabulated and can be calculated from the parameters in Table 16 and the values of x The sum of the individual logarithmic probability ratios is -11,584, or a probability of being exempt from the disease versus having the disease of 107,386, where the probability e11 584 = 107,386. The first 3 biomarker values have more consistent probabilities with the disease group (log> 0 probability) but it has been uniformly discovered that the remaining 7 biomarkers favor the control group. The multiplication of the probabilities with each other provides the same results as shown above; a probability of 107,386 that the unknown sample is exempt from the disease. In fact, this sample came from the control population in the training set.

Example 4. Avid algorithm for selecting biomarker panels with respect to classifiers.

This example describes the selection of biomarkers in Table 1 to form panels that can be used as classifiers in any of the methods described herein. Subsets of the biomarkers were selected in Table 1 to build classifiers with good performance. This method was also used to determine which potential markers were included as biomarkers in Example 2.

The measure of classifier performance used in this document is the ABC; a yield of 0.5 is the baseline expectation for a random classifier (coin toss), a classifier worse than the random one would have a score between 0.0 and 0.5, a classifier with better performance than the random one would have a score between 0.5 and 1.0. A perfect classifier without errors would have a sensitivity of 1.0 and a specificity of 1.0. The methods described in Example 4 can be applied to other usual measures of performance such as measure F, the sum of sensitivity and specificity or the product of sensitivity and specificity. One might specifically want to treat sensitivity and specificity with different weight, to select the classifiers that perform more specifically at the cost of some sensitivity or to select the classifiers that perform more sensitively at the cost of some specificity. Since the method described here only involves a "performance" measure, any weighting scheme that results in a single performance measure can be used. Different applications will have different benefits for true positive and true negative findings and also different costs associated with false positive findings with respect to false negative findings. For example, the exploration of asymptomatic smokers and the differential diagnosis of benign nodules found on CT will not generally have the same optimal counterpart between specificity and sensitivity. The different demands of the two trials will generally require determining different weighting for positive and negative misclassifications, which is reflected in the performance measure. Changing the performance measure will generally change the exact subset of markers selected from Table 1 for a given data set.

For the Bayesian approach for the differentiation of NSCLC samples from control samples described in Example 3, the classifier was completely parameterized by the biomarker distributions in the disease and benign training samples and the biomarker list was chosen from the Table one; that is, the subset of markers chosen for inclusion determined a classifier in a one-to-one manner given a set of training data.

The avid method used here was used to find the optimal subset of markers in Table 1. For small numbers of markers or classifiers with relatively few markers, each possible subset of markers was listed and evaluated with respect to the performance of the classifier constructed with that particular set of markers (see Example 4, Part 2). (This approach is well known in the field of statistics as "best subset selection"; see, for example, Hastie et a /). However, for the classifiers described herein, the number of combinations of multiple markers can be very large and it was not feasible to evaluate each possible set of 10 markers, since there are 30,045,015 possible combinations that can be generated from a list of only 30 total analytes. Due to the unfeasibility of searching in each subset of markers, the individual optimal subset may not be found; however, using this approach, many excellent subsets were discovered and, in many cases, any of these subsets may represent an optimal one.

Instead of evaluating each possible set of markers, a direct “avid” approach can be followed (see, for example, Dabney AR, Storey JD (2007) Optimality Driven Nearest Centroid Classification from Genomic Data.PLoS ONE 2 (10): e1002. doi: 10.1371 / journal.pone.0001002). Using this method, a classifier with the best individual marker (based on the KS distance for individual markers) is started and allowed to grow in each

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stage testing, in turn, each member of a list of bookmarks that is not currently a member of the set of bookmarks in the classifier. The marker that scores best in combination with the existing classifier is added to the classifier. This is repeated until no further improvement in performance is achieved. Unfortunately, this approach can obviate valuable combinations of markers for which some of the individual markers have not all been chosen before the process is stopped.

The avid procedure used here was an elaboration of the preceding direct stage approach, because, to broaden the search, instead of maintaining only a single candidate classifier (subset of markers) at each stage, a list of candidate classifiers was maintained. The list was seeded with each possible subset of individual markers (using each marker in the table alone). The list was expanded in stages obtaining new classifiers (subsets of markers) from those currently in the list and adding them to the list. Each subset of markers currently in the list was extended by adding any marker from Table 1 that is no longer part of that classifier, and which, after its addition to the subset, would not duplicate an existing subset (these are called "allowable markers"). Each existing subset of bookmarks was extended by each allowable bookmark in the list. Clearly, this process would generate over time every possible subset and the list would run out of space. Therefore, all generated classifiers were maintained only as long as the list was smaller than a certain predetermined size (often enough to contain the three marker subsets). Once the list reached the default size limit, it became elitist; that is, only the classifiers that showed a certain level of performance remained on the list, and the others left the list and were lost. This was achieved by keeping the list ranked in order of classifier performance; new classifiers were inserted that were at least as good as the worst classifier currently on the list, forcing the expulsion of the lowest current performance. An additional implementation detail is that the list was completely replaced at each generational stage; therefore, each classifier in the list had the same number of markers and at each stage the number of markers per classifier grew by one.

Since this method produced a list of candidate classifiers using different combinations of markers, it can be asked whether the classifiers can be combined to avoid errors that could be made by the best individual classifier or minority groups of the best classifiers. Such "joint" and "expert committee" methods are well known in the fields of statistical and machine learning and include, for example, "Averaging," "Voting," "Stacking," "Aggregation," and "Reinforcement." see, for example, Hastie et al.). These combinations of simple classifiers provide a method to reduce variance in classifications due to noise in any particular set of markers by including several different classifiers and therefore information from a larger set of markers in the biomarker table, averaging Effectively among the classifiers. An example of the usefulness of this approach is that it can prevent outliers in a single marker from adversely affecting the classification of a single sample. The requirement to measure a large number of signals may be impractical in conventional "one marker at a time" antibody assays but has no disadvantages for a multiple aptamer assay. Techniques such as these take advantage of a more extensive table of biomarkers and use the multiple sources of information regarding disease processes to provide a more robust classification.

The biomarkers selected in Table 1 give rise to classifiers that perform better than classifiers constructed with "non-markers" (ie, proteins that have signals that did not meet the criteria for inclusion in Table 1 (as described in Example 2) ).

For classifiers containing only one, two and three markers, all possible classifiers obtained using the biomarkers in Table 1 were listed and examined for performance distribution compared to classifiers constructed from a similar table of non-signals. randomly selected markers.

In Figure 11, ABC was used as the measure of performance; a yield of 0.5 is the baseline expectation for a random classifier (coin toss). The classifier performance histogram was compared with the performance histogram from a similar exhaustive enumeration of classifiers constructed from a "non-marker" table of 59 non-marker signals; The 59 signals were randomly selected from aptamers that did not demonstrate differential signaling between control populations and disease.

Figure 11 shows histograms of the performance of all classifiers of one, two and three possible markers constructed from the biomarker parameters in Table 14 for biomarkers that can differentiate between the control group and NSCLC and compares these classifiers with all classifiers of one, two and three possible markers constructed using the 59 UFR signals of "non-marker" aptamer. Figure 11A shows the performance histograms of single marker classifiers, Figure 11C shows the performance histogram of two marker classifiers and Figure 11C shows the performance histogram of three marker classifiers.

In Figure 11, the solid lines represent the histograms of classifier performance of all classifiers of one, two and three markers using biomarker data for smokers and nodules.

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Benign lung and NSCLC in Table 14. Dashed lines are the histograms of classifier performance of all classifiers of one, two and three markers using the data for controls and NSCLC but using the set of random non-marker signals.

The classifiers constructed from the markers listed in Table 1 form a defined histogram, well separated from the classifiers constructed with "non-marker" signals for all comparisons of one marker, two markers and three markers. The performance and ABC score of the classifiers constructed from the biomarkers in Table 1 also increase faster with the number of markers than the classifiers constructed from the non-markers, the separation increases between the marker and non-marker classifiers. as the number of markers per classifier increases. All classifiers constructed using the biomarkers listed in Table 14 perform remarkably better than classifiers constructed using "non-markers."

Classifier performance distributions show that there are many possible multiple marker classifiers that can be obtained from the set of analytes in Table 1. Although some biomarkers are better than others alone, as evidenced by the distribution of classifier and ABC scores for analytes individual, it was desirable to determine whether such biomarkers are necessary to build high performance classifiers. To make this determination, the performance of the classifier performance was examined leaving out several of the best biomarkers. Figure 12 compares the performance of constructed classifiers with the complete list of biomarkers in Table 1 with the performance of classifiers constructed with subsets of biomarkers in Table 1 that excluded better classification markers.

Figure 12 demonstrates that classifiers constructed without the best markers performed well, which implies that the performance of the classifiers was not due to some small central group of markers and that changes in the underlying processes associated with disease are reflected in the activities of many proteins Many subsets of the biomarkers in Table 1 performed almost optimally, even after removing the top 15 of the 59 markers in Table 1. After discarding the 15 best ranked markers (sorted by KS distance) from Table 1, the performance of classifier increased with the number of markers selected from the table until reaching an ABC of almost 0.93, close to the performance of the optimal classifier score of 0.948 selected from the complete list of biomarkers.

Finally, Figure 13 shows how the ROC performance of typical classifiers constructed from the list of parameters in Table 14 according to Example 3. A five analyte classifier was constructed with MMP7, CLIC1, STX1A, CHRDL1 and PA2G4. Figure 13A shows the performance of the model, assuming independence from these markers, as in Example 3 and Figure 13B shows the empirical ROC curves generated from the study data sets used to define the parameters in Table 14. It can be seen that the performance for a given number of selected markers was qualitatively agreed, and that quantitative agreement was generally quite good, as demonstrated by the ABCs, although the model calculation tends to overestimate the classifier performance. This is consistent with the notion that the information provided by any particular biomarker regarding disease processes is redundant with the information provided by other biomarkers provided in Table 1 while the model calculation assumes complete independence. Figure 13 therefore demonstrates that Table 1 in combination with the methods described in Example 3 allows the construction and evaluation of a large number of classifiers useful for the differentiation of NSCLC from the control group.

Example 5. Panel of clinical biomarkers

A random forest classifier was constructed from a panel of selected biomarkers that may be the most appropriate for use in a clinical diagnostic trial. Unlike the models selected by the simple avid Bayesian direct algorithm, the random forest classifier does not assume that biomarker measurements are distributed randomly. Therefore this model can use biomarkers from Table 1 that are not effective in the simple Bayesian classifier.

The panel was selected using a reverse removal procedure that used the gini importance measurement provided by the random forest classifier. The importance of gini is a measure of the effectiveness of a biomarker in the correct classification of samples in the training set.

This measure of importance of the biomarker can be used to eliminate markers that are less vital to the performance of the classifier. The reverse elimination procedure was initiated by constructing a random forest classifier that included all 59 in Table 1. The less important biomarker was later removed and a new model was constructed with the remaining biomarkers. This procedure continued until only individual biomarkers remained.

The final panel that was selected provided the best balance between the highest ABC and the lowest number of markers in the model. The panel of 8 biomarkers that met these criteria is composed of the following analytes, MMP12, MMP7, KLK3-SERPINA3, CRP, C9, CNDP1, CA6 and EGFR. A representation of the

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ROC curve for this biomarker panel in Figure 14. The sensitivity of this model is 0.70 with a corresponding specificity of 0.89.

Example 6. Biomarkers for the diagnosis of cancer

The identification of potential biomarkers for the general diagnosis of cancer was performed. Both case and control samples of 3 different types of cancer (lung cancer, mesothelioma and renal cell carcinoma) were evaluated. In all sites, the inclusion criteria were at least 18 years old with signed informed consent. Both cases and controls for known malignancies other than the cancer in question were excluded.

Lung cancer. Case and control samples were obtained as described in Example 2. A total of 46 cases and 218 controls were used in this Example.

Pleural Mesothelioma Case and control samples were obtained from a biodeposit of academic cancer center to identify potential markers for the differential diagnosis of benign lung disease pleural mesothelioma, including suspicious radiological findings that were subsequently diagnosed as non-malignant. A total of 124 cases of mesothelioma and 138 controls exposed to asbestos were used in this Example.

Renal cell carcinoma. Case and control samples of a biodeposit of academic cancer center were obtained from patients with renal cell carcinoma (RCC) and benign masses (BEN). Presurgical samples (TP1) were obtained for all subjects. The primary analysis compared outcome data (as recorded in the SEER database field State of CA 1) for patients with CRC with "disease tests" (PRE) versus "without disease tests" (SPE) documented by clinical follow-up. A total of 38 PRE cases and 104 SPE controls were used in this Example.

A final list of cancer biomarkers was identified by combining the sets of biomarkers considered for each of the 3 different cancer studies. Bayesian classifiers were constructed successively using sets of biomarkers of increasing size using an avid algorithm (as described in more detail in section 6.2 of this Example). The sets (or panels) of biomarkers that were useful for diagnosing cancer in general between different sites and types of cancer were compiled as a function of the size of the set (or panel) and analyzed for their performance. This analysis resulted in the list of 23 cancer biomarkers shown in Table 19, each of which was present in at least one of these sets of successive markers, ranging in size from three to ten markers. As an illustrative example, the generation of a specific panel composed of ten cancer biomarkers is described, which is shown in Table 32.

6.1 Simple Bayesian Classification for Cancer

From the list of biomarkers in Table 1, a panel of ten potential cancer biomarkers was selected using an avid algorithm for biomarker selection, as outlined in Section 6.2 of this Example. A different simple Bayesian classifier was constructed for each of the 3. Class-dependent probability density (fdp) functions, p (x¡ | c) and p (x ^ d), where Xi is the log of the value of UFR measured for biomarker i, ycyd refer to the control and disease populations, they were modeled as log-normal distribution functions characterized by a median and variance a2 The parameters for fdp of the 3 models composed of the ten potential biomarkers are listed in Table 31

The simple Bayesian classification for this model is provided by the following equation, where p (d) is the prevalence of the disease in the population

image9

+ ln

(P (d) \

V1 - P (d) J

appropriate for the test yn = 10. Each of the terms in the sum is a logarithmic probability relation for an individual marker and the total logarithmic probability relation that a sample x is exempt from the disease of interest (that is, in in this case, each particular disease of the 3 different types of cancer) in front of the disease is simply the sum of these individual terms plus a term that counts

The prevalence of the disease. For simplicity, it is assumed that p (d) = 0.5 so that

Given an unknown sample measurement in log (UFR) for each of the ten biomarkers of 9.5, 8.8, 7.8, 8.3, 9.4, 7.0, 7.9, 6.3 , 7,7, 10,6, the calculation of the classification is detailed in Table 32. The individual components that comprise the logarithmic probability relationship for disease class versus control are tabulated and

image10

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they can be calculated from the parameters in Table 31 and the values of x. The sum of the individual logarithmic probability ratios is -3,326, or a probability of being exempt from the disease versus having the disease of 28, where the probability e3326 = 28. The first 4 biomarker values have more consistent probabilities with the group of disease (log probability> 0) but it has been uniformly discovered that the remaining 6 biomarkers favor the control group. The multiplication of the probabilities with each other provides the same results as shown above; a probability of 28 that the unknown sample is exempt from the disease. In fact, this sample came from the control population in the renal cell carcinoma training set.

6.1 Simple Bayesian Classification for Cancer

From the list of biomarkers in Table 1, a panel of ten potential cancer biomarkers was selected using an avid algorithm for biomarker selection, as outlined in Section 6.2 of this Example. A different simple Bayesian classifier was constructed for each of the 3 different types of cancer. The class-dependent probability density (fdp) functions, p (x \ c) and p (x ^ d), where Xi is the log of the measured UFR value for biomarker i, ycyd refer to the control populations and disease, were modeled as log-normal distribution functions characterized by a mean p and variance a2 The parameters for fdp of the 3 models composed of the ten potential biomarkers are listed in Table 31.

The simple Bayesian classification for this model is provided by the following equation, where p (d) is the prevalence of the disease in the population,

ln

image11

image12

image13

image14

image15

+ ln

(p {d) \

\ l-p (d) J

appropriate for the test and n = 10. Each of the terms in the sum is a logarithmic probability relation for an individual marker and the total logarithmic probability relation that a sample jf is exempt from the disease of interest (that is, in In this case, each particular disease of the 3 different types of cancer) in front of the disease is simply the sum of these individual terms plus a term that accounts for the prevalence of the disease. For simplicity, it is assumed that p (d) = 0.5 so that

image16

Given an unknown sample measurement in log (UFR) for each of the ten biomarkers of 9.5, 8.8, 7.8, 8.3, 9.4, 7.0, 7.9, 6.3 , 7,7, 10,6, the calculation of the classification is detailed in Table 32. The individual components that comprise the logarithmic probability relationship for disease class versus control are tabulated and can be calculated from the parameters in the Table 31 and the values of x The sum of the individual logarithmic probability ratios is -3,326, or a probability of being exempt from the disease versus having the disease of 28, where the probability e3326 = 28. Only 4 of the values of Biomarkers have more consistent probabilities with the disease group (log> 0 probability) but it has been uniformly discovered that the remaining 6 biomarkers favor the control group. The multiplication of the probabilities with each other provides the same results as shown above; a probability of 28 that the unknown sample is exempt from the disease. In fact, this sample came from the control population in the CPNM training set.

6.2 Avid algorithm for selecting panels of cancer biomarkers with respect to class 1 classifiers

Subsets of the biomarkers were selected in Table 1 to construct potential classifiers that could be used to determine which of the markers could be used as biomarkers of general cancer to detect cancer.

Given a set of markers, a defined model was trained for each of the 3 cancer studies, so that a comprehensive measure of performance was necessary to select a set of biomarkers that were able to simultaneously classify many different types of cancer. The measure of classifier performance used here was the average area under the ROC curve in all simple Bayesian classifiers. The ROC curve is the representation of the true positive rate (sensitivity) versus the false positive rate (1- specificity) of a single classifier. The area under the ROC curve (ABC) ranges from 0 to 1.0, where an ABC of 1.0 corresponds to a perfect classification and an ABC of 0.5 corresponds to a random classifier (coin toss). Other common performance measures may be applied such as measure F or the sum or product

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of sensitivity and specificity. Specifically, one might wish to treat sensitivity and specificity with different weights, to select the classifiers that perform more specifically at the cost of some sensitivity or to select the classifiers that perform more sensitively at the cost of specificity. The inventors choose to use ABC because it encompasses all combinations of sensitivity and specificity in a single measure. Different applications will have different benefits for true positive and true negative findings and will have different costs associated with false positive findings with respect to false negative findings. Changing the performance measure can change the exact subset of selected markers for a given data set.

For the Bayesian approach to the differentiation of cancer samples from control samples described in Section 6.1 of this Example, the classifier was completely parameterized by the biomarker distributions in each of the 3 cancer studies and the biomarker list was chosen from Table 19. That is, the subset of markers chosen for inclusion determined a classifier in a one-to-one manner given a set of training data.

The avid method used here was used to find the optimal subset of markers in Table 1. For small numbers of markers or classifiers with relatively few markers, each possible subset of markers was listed and evaluated with respect to the performance of the classifier constructed with that particular set of markers (see Example 4). (This approach is well known in the field of statistics as "selection of a better subset"; see, for example, Hastie et al). However, for the classifiers described herein, the number of combinations of multiple markers can be very large and it was not feasible to evaluate each possible set of 10 markers, since there are 30,045,015 possible combinations that can be generated from a list of only 30 total analytes. Due to the unfeasibility of searching in each subset of markers, the individual optimal subset may not be found; however, using this approach, many excellent subsets were discovered and, in many cases, any of these subsets may represent an optimal one.

Instead of evaluating each possible set of markers, a direct “avid” approach can be followed (see, for example, Dabney AR, Storey JD (2007) Optimality Driven Nearest Centroid Classification from Genomic Data.PLoS ONE 2 (10): e1002. doi: 10.1371 / journal.pone.0001002). Using this method, a classifier with the best individual marker (based on the KS distance for the individual markers) is started and allowed to grow at each stage by testing, in turn, each member of a list of markers that is not currently a member of the set of markers in the classifier. The marker that scores best in combination with the existing classifier is added to the classifier. This is repeated until no further improvement in performance is achieved. Unfortunately, this approach can obviate valuable combinations of markers for which some of the individual markers have not all been chosen before the process is stopped.

The avid procedure used here was an elaboration of the preceding direct stage approach, because, to broaden the search, instead of maintaining only a single subset of markers at each stage, a list of candidate marker sets was maintained. The list was seeded with a list of individual markers. The list was expanded in stages by obtaining new subsets of markers from those currently in the list and adding them to the list. Each subset of markers currently in the list was extended by adding any marker from Table 1 that is no longer part of that classifier, and which, after its addition to the subset, would not duplicate an existing subset (these are called "allowable markers"). Each time a new set of markers was defined, a set of classifiers consisting of one for each cancer study was trained using these markers and the overall performance was measured by the average ABC in the 3 studies. To avoid potential overfitting, the ABC for each cancer study model was calculated using a 10-fold cross-validation procedure. Each existing subset of bookmarks was extended by each allowable bookmark in the list. Clearly, this process would generate over time every possible subset and the list would run out of space. Therefore, all sets of generated bookmarks were maintained only while the list was smaller than a certain predetermined size. Once the list reached the default size limit, it became elitist; that is, only the sets of classifiers that showed a certain level of performance remained on the list, and the others left the list and were lost. This was achieved by keeping the list ranked in order of classifier set performance; new sets of markers were inserted whose classifiers were globally at least as good as the worst set of classifiers currently on the list, forcing the expulsion of the current lower performance classifier sets. An additional implementation detail is that the list was completely replaced at each generational stage; therefore, each set of markers in the list had the same number of markers and at each stage the number of markers per classifier grew by one.

In one embodiment, the set (or panel) of biomarkers useful for constructing classifiers to diagnose general cancer with respect to absence of cancer is based on the average ABC for the particular combination of biomarkers used in the classification scheme. Many combinations of biomarkers obtained from the markers were identified in Table 19 that were able to effectively classify different control cancer samples. Representative panels are shown in Tables 22-29, which expose a series of 100 different panels of 3-10 biomarkers, which have the average cross-validation ABC (VC) indicated for each

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panel. The total number of occurrences of each marker on each of these panels is indicated at the bottom of each table.

The biomarkers selected in Table 19 give rise to classifiers that perform better than classifiers constructed with "non-markers." Figure 15 shows the performance of the classifiers of ten biomarkers of the inventors compared to the performance of other possible classifiers.

Figure 15A shows the distribution of average AUCs for classifiers constructed from randomly sampled sets of ten “non-markers” taken from the complete set of 23 present in the 3 studies, excluding the ten markers in Table 19. The performance of the ten Potential cancer biomarkers is presented as a dashed vertical line. This representation clearly shows that the performance of the ten potential biomarkers is much greater than the distribution of other combinations of biomarkers.

Figure 15B presents a similar distribution to Figure 15A, however the randomly sampled sets were restricted to the 49 biomarkers of Table 1 that were not selected by the avid biomarker selection procedure for ten analyte classifiers. This representation demonstrates that the ten markers selected by the avid algorithm represent a subset of biomarkers that generalize for other types of cancer much better than classifiers constructed with the remaining 49 biomarkers.

Finally, Figure 16 shows the classifier ROC curve for each of the 3 classifiers of cancer studies. The embodiments and the above examples are intended to be examples only. No particular embodiment, example or element of a particular embodiment or example should be construed as a critical, required or essential element or characteristic of any of the claims. In addition, no element described herein is necessary for the practice of the appended claims unless expressly described as "essential" or "critical." Various alterations, modifications, substitutions and other variations may be made to the disclosed embodiments without departing from the scope of the present application, which is defined by the appended claims. The descriptive report, including the figures and examples, should be interpreted in an illustrative manner, rather than a restrictive one, and it is intended that all such modifications and substitutions be included in the scope of the application. Consequently, the scope of the request should be determined by the appended claims and their legal equivalents, rather than by the examples provided above. For example, steps indicated in any of the claims of the method or process may be executed in any feasible order and are not limited to an order presented in any of the embodiments, the examples or the claims. In addition, in any of the methods mentioned above, one or more biomarkers of Table 1 or Table 19 can be specifically excluded as an individual biomarker or as a biomarker of any panel.

Table 1: Cancer biomarkers

 Column # 1

 Column # 2 Column # 3 Column # 4 Column # 5 Column # 6

 Biomarker No.

 Entrez Symbol (s) Gene biomarker designation ID Entrez Gene ID SwissProt Public name Address

 one

 AHSG 197 P02765 a2-HS-Descending Glycoprotein

 2

 AKR7A2 8574 043488 Aflatoxin B1 aldehyde reductase Ascending

 3

 AKT3 10000 Q9Y243 PKB and Ascending

 4

 ASGR1 432 P07306 ASGPR1 Descending

 5

 BDNF 627 P23560 BDNF Descending

 6

 BMP1 649 P13497 BMP-1 Descending

 7

 BMPER 168667 Q8N8U9 BMPER Descending

 8

 C9 735 P02748 C9 Ascending

 9

 CA6 765 P23280 Carbonic Anhydrase VI Descending

 10

 CAPG 822 P40121 CapG Descending

 eleven

 CDH1 999 P12830 Cadherina-1 Descending

 12

 CHRDL1 91851 Q9BU40 Cord type 1 Ascending

 13

 CKB-CKM-1152; P12277; P06732 CK-MB Descending

 1158

 14

 CLIC1 1192 000299 chloride intracellular channel 1 Ascending

 fifteen

 CMA1 1215 P23946 Quimasa Descending

 16

 CNTN1 1272 Q12860 Contactina-1 Descending

 17

 COL18A1 80781 P39060 Ascending Endostatin

 18

 CRP 1401 P02741 CRP Ascending

 19

 CTSL2 1515 060911 Cathepsin V Descending

 twenty

 DDC 1644 P20711 dopa decarboxylase Descending

 twenty-one

 EGFR 1956 P00533 ERBB1 Descending

 22

 FGA-FGB-FGG 2243; P02671; P02675; Dimer D Ascending

 2244; P02679

 2266

 2. 3

 FN1 2335 P02751 Fibronectin FN1.4 Descending

 24

 GHR 2690 P10912 Growth hormone receptor Descending

 25

 GPI 2821 P06744 glucose phosphate isomerase Ascending

 26

 HMGB1 3146 P09429 HMG-1 Ascending

 27

 HNRNPAB 3182 Q99729 hnRNP A / B Ascending

 28

 HP 3240 P00738 Haptoglobin, Mixed Type Ascending

 29

 HSP90AA1 3320 P07900 HSP90a Ascending

 30

 HSPA1A 3303 P08107 HSP 70 Ascending

 31

 IGFBP2 3485 P18065 IGFBP-2 Ascending

 32

 IGFBP4 3487 P22692 IGFBP-4 Ascending

 33

 IL12B-IL23A 3593; 51561 P29460; Q9NPF7 IL-23 Ascending

 3. 4

 ITIH4 3700 Q14624 Heavy chain of inter-or-trypsin inhibitor H4 Ascending

 35

 KIT 3815 P10721 SCF sR Descending

 36

 KLK3-SERPINA3 354; 12 P07288; P01011 PSA-ACT Ascending

 37

 L1CAM 3897 P32004 NCAM-L1 Descending

 38

 LRIG3 121227 Q6UXM1 LRIG3 Descending

 39

 MMP12 4321 P39900 MMP-12 Ascending

 40

 MMP7 4316 P09237 MMP-7 Ascending

 41

 NME2 4831 P22392 NDP kinase B Ascending

 42

 PA2G4 5036 Q9UQ80 ErbB3 binding protein Ebpl Ascending

 43

 PLA2G7 7941 Q13093 LpPLA2 / PAFAH Descending

 44

 PLAUR 5329 Q03405 suPAR Ascending

 Four. Five

 PRKACA 5566 P17612 PRKA C-a Ascending

 46

 PRKCB 5579 P05771 PKC-p-II Descending

 47

 PROK1 84432 P58294 EG-VEGF Descending

 48

 PRSS2 5645 P07478 Trypsin-2 Ascending

 49

 PTN 5764 P21246 Ascending Pleiotropin

 fifty

 SERPINA1 5265 P01009 a1-ascending antitrypsin

 51

 STC1 6781 P52823 Staniocalcin-1 Ascending

 52

 STX1A 6804 Q16623 Syntaxin 1A Descending

 53

 TACSTD2 4070 P09758 Protein GA733-1 Descending

 54

 TFF3 7033 Q07654 Trefoil Factor 3 Ascending

 55

 TGFBI 7045 Q15582 pIGH3 Descending

 56

 TPI1 7167 P60174 Ascending Triosaphosphate Isomerase

 57

 TPT1 7178 P13693 Fortilina Ascending

 58

 YWHAG 7532 P61981 14-3-3 Protein and Ascending

 59

 YWHAH 7533 Q04917 14-3-3 eta protein Ascending

Table 2: 1 biomarker panels

 Markers

 VC ABC

 one

 YWHAG 0.840

 2

 MMP7 0.804

 3

 CLICK1 0.803

 4

 MMP12 0.773

 5

 STX1A 0.771

 6

 C9 0.769

 7

 LRIG3 0.769

 8

 EGFR 0.767

 9

 TPT1 0.760

 10

 CMA1 0,758

 eleven

 YWHAH 0,756

 12

 GPI 0,752

 13

 BMP1 0,751

 14

 DDC 0.747

 fifteen

 NME2 0.745

 16

 IGFBP2 0.743

 17

 FGA-FGB-FGG 0.741

 18

 CAPG 0.738

 19

 AKR7A2 0.733

 twenty

 HNRNPAB 0.730

 twenty-one

 CDH1 0.728

 22

 HSP90AA1 0.726

 2. 3

 CKB-CKM 0.724

 24

 CRP 0.724

 25

 PTN 0.723

 26

 BMPER 0.721

 27

 TPI1 0.720

 28

 TGFBI 0.720

 29

 KIT 0.717

 30

 HP 0.715

 31

 KLK3-SERPINA3 0.713

 32

 PLAUR 0.711

 33

 GHR 0.705

 3. 4

 CA6 0.705

 35

 PRKACA 0.704

 36

 COL18A1 0.701

 37

 HMGB1 0,700

 38

 IGFBP4 0.688

 39

 AKT3 0.697

 40

 AHSG 0.697

 41

 CTSL2 0.694

 42

 TACSTD2 0.690

 43

 FN1 0.690

 44

 IL12B-IL23A 0.690

 Four. Five

 BDNF 0.689

 46

 L1CAM 0.688

 47

 SERPINA1 0.688

 48

 PROK11 0.684

 49

 PRKCB 0.684

 fifty

 STC1 0.682

 51

 CHRDL1 0.679

 52

 TFF3 0.678

 53

 PRSS2 0.663

 54

 ASGR1 0.660

 55

 HSPA1A 0.658

 56

 PA2G4 0.655

 57

 CNTN1 0.648

 58

 ITIH4 0.635

 59

 PLA2G7 0.631

Table 3: Panels of 2 biomarkers

 Markers

 VC ABC

 one

 MMP7 YWHAG 0.878

 2

 C9 YWHAG 0.876

 3

 STX1A YWHAG 0.874

 4

 MMP7 CLIC1 0.874

 5

 LRIG3 YWHAG 0.871

 6

 KLK3-SERPINA3 YWHAG 0.867

 7

 YWHAG CRP 0.867

 8

 BMP1 YWHAG 0.866

 9

 MMP12 CLIC1 0.865

 10

 TGFBI YWHAG 0.864

 eleven

 KLK3-SERPINA3 CLICK1 0.863

 12

 YWHAG L1CAM 0.863

 13

 STX1A CLICK1 0.863

 14

 SERPINA1 YWHAG 0.862

 fifteen

 CMA1 YWHAG 0.862

 16

 NME2 FGA-FGB-FGG 0.861

 17

 CA6 YWHAG 0.859

 18

 MMP7 AKR7A2 0.859

 19

 DDC YWHAG 0.858

 twenty

 C9 CLIC1 0.857

 twenty-one

 MMP7 NME2 0.857

 22

 CKB-CKM YWHAG 0.857

 2. 3

 FGA-FGB-FGG CLIC1 0.856

 24

 BMP1 CLIC1 0.856

 25

 EGFR YWHAG 0.856

 26

 AHSG YWHAG 0.855

 27

 YWHAG MMP12 0.855

 28

 MMP7 TPI1 0.855

 29

 0.8W YWHAG KIT

 30

 LRIG3 CLICK1 0.854

 31

 HP YWHAG 0.854

 32

 PLAUR YWHAG 0.854

 33

 CMA1 CLIC1 0.853

 3. 4

 BDNF YWHAG 0.853

 35

 EGFR CLICK1 0.853

 36

 MMP7 TPT1 0.852

 37

 YWHAG CLICK1 0.851

 38

 PTN YWHAG 0.850

 39

 BDNF CLIC1 0.849

 40

 IGFBP2 YWHAG 0.849

 41

 MMP7 GPI 0.849

 42

 CNTN1 YWHAG 0.849

 43

 BMPER YWHAG 0.848

 44

 YWHAG FGA-FGB-FGG 0.847

 Four. Five

 MMP7 HNRNPAB 0.847

 46

 C9 GPI 0.847

 47

 YWHAG GPI 0.846

 48

 L1CAM MMP12 0.846

 49

 YWHAG ITIH4 0.846

 fifty

 GHR YWHAG 0.846

 51

 YWHAG HNRNPAB 0.846

 52

 MMP7 CMA1 0.846

 53

 C9 NME2 0.845

 54

 MMP7 LRIG3 0.845

 55

 IGFBP2 CLIC1 0.845

 56

 COL18A1 YWHAG 0.845

 57

 CHRDL1 CLICK1 0.845

 58

 CDH1 MMP7 0.844

 59

 PLAUR CLICK1 0.844

 60

 TPI1 FGA-FGB-FGG 0.844

 61

 CHRDL1 YWHAG 0.844

 62

 MMP7 PRKACA 0.844

 63

 C9 AKR7A2 0.843

 64

 YWHAG PLA2G7 0.843

 65

 KLK3-SERPINA3 TPT1 0.843

 66

 BMP1 GPI 0.843

 67

 KLK3-SERPINA3 MMP7 0.842

 68

 C9 TPT1 0.842

 69

 COL18A1 CLICK1 0.842

 70

 YWHAG AKR7A2 0.842

 71

 YWHAG STC1 0.842

 72

 MMP7 TGFBI 0.842

 73

 AKR7A2 MMP12 0.842

 74

 MMP7 YWHAH 0.842

 75

 HMGB1 MMP7 0.841

 76

 TPT1 FGA-FGB-FGG 0.841

 77

 GHR CLICK1 0.841

 78

 KLK3-SERPINA3 STX1A 0.840

 79

 LRIG3 TPT1 0.840

 80

 STX1A MMP12 0.840

 81

 YWHAG PRSS2 0.840

 82

 DDC CLIC1 0.840

 83

 CLP CLIC1 0.840

 84

 HMGB1 YWHAG 0.840

 85

 STX1A TPT1 0.839

 86

 CDH1 YWHAG 0.839

 87

 STX1A GPI 0.839

 88

 KLK3-SERPINA3 NME2 0.838

 89

 LRIG3 YWHAH 0.838

 90

 AKR7A2 FGA-FGB-FGG 0.838

 91

 C9 HNRNPAB 0.837

 92

 TACSTD2 YWHAG 0.837

 93

 YWHAG TPI1 0.837

 94

 STX1A NME2 0.836

 95

 KLK3-SERPINA3 AKR7A2 0.836

 96

 LRIG3 AKR7A2 0.836

 97

 NME2 MMP12 0.836

 98

 CAPG CLICK1 0.836

 99

 YWHAG NME2 0.836

 100

 MMP7 STX1A 0.835

Table 4: 3 biomarker panels

 Markers

 VC ABC

 one

 KLK3-SERPINA3 MMP7 CLICK1 0.896

 2

 KLK3-SERPINA3 STX1A CLICK1 0.895

 3

 KLK3-SERPINA3 STX1A YWHAG 0.895

 4

 MMP7 C9 YWHAG 0.895

 5

 MMP7 YWHAG CLICK1 0.894

 6

 C9 STX1A YWHAG 0.893

 7

 MMP7 LRIG3 YWHAG 0.893

 8

 MMP7 TGFBI YWHAG 0.893

 9

 MMP7 CMA1 CLIC1 0.893

 10

 BDNF MMP7 CLIC1 0.892

 eleven

 MMP7 GHR CLICK1 0.892

 12

 CDH1 MMP7 YWHAG 0.892

 13

 BDNF C9 CLIC1 0.892

 14

 STX1A YWHAG CRP 0.892

 fifteen

 MMP7 YWHAG TPI1 0.892

 16

 MMP7 STX1A YWHAG 0.892

 17

 TGFBI STX1A YWHAG 0.891

 18

 LRIG3 YWHAG CRP 0.891

 19

 MMP7 YWHAG L1CAM 0.891

 twenty

 MMP7 YWHAG PA2G4 0.891

 twenty-one

 C9 LRIG3 YWHAG 0.890

 22

 STX1A MMP12 CLIC1 0.890

 2. 3

 MMP7 LRIG3 CLIC1 0.890

 24

 KLK3-SERPINA3 MMP7 YWHAG 0.890

 25

 MMP7 BMP1 CLIC1 0.890

 26

 BDNF STX1A CLIC1 0.890

 27

 MMP7 STX1A CLICK1 0.889

 28

 MMP7 BMP1 YWHAG 0.899

 29

 HMGB1 MMP7 YWHAG 0.899

 30

 SERPINA1 STX1A YWHAG 0.899

 31

 MMP7 YWHAG GPI 0.899

 32

 MMP7 CMA1 YWHAG 0.899

 33

 MMP7 YWHAG NME2 0.889

 3. 4

 MMP7 C9 CLIC1 0.889

 35

 C9 CMA1 YWHAG 0.888

 36

 MMP7 YWHAG CRP 0.888

 37

 KLK3-SERPINA3 CNTN1 YWHAG 0.888

 38

 MMP7 YWHAG AKR7A2 0.887

 39

 MMP7 ITIH4 CLICK1 0.887

 40

 CDH1 MMP7 CLIC1 0.887

 41

 KLK3-SERPINA3 MMP7 AKR7A2 0.887

 42

 MMP7 GHR YWHAG 0.887

 43

 KLK3-SERPINA3 CHRDL1 CLICK1 0.887

 44

 KLK3-SERPINA3 LRIG3 YWHAG 0.887

 Four. Five

 BMP1 STX1A CLIC1 0.887

 46

 C9 STX1A CLICK1 0.887

 47

 MMP7 GPI CLICK1 0.887

 48

 TGFBI LRIG3 YWHAG 0.886

 49

 IGFBP2 MMP7 YWHAG 0.886

 fifty

 MMP7 CKB-CKM YWHAG 0.886

 51

 LRIG3 STX1A YWHAG 0.886

 52

 GHR STX1A CLIC1 0.886

 53

 MMP7 DDC YWHAG 0.886

 54

 BMP1 STX1A YWHAG 0.886

 55

 MMP7 DDC CLIC1 0.886

 56

 C9 CHRDL1 CLICK1 0.885

 57

 MMP7 C9 AKR7A2 0.885

 58

 BDNF MMP7 YWHAG 0.885

 59

 MMP7 KIT YWHAG 0.885

 60

 MMP7 TGFBI CLIC1 0.885

 61

 BDNF IGFBP2 CLIC1 0.885

 62

 MMP7 YWHAG ITIH4 0.885

 63

 MMP7 YWHAG HNRNPAB 0.885

 64

 KLK3-SERPINA3 LRIG3 CLICK1 0.885

 65

 MMP7 HP YWHAG 0.885

 66

 HMGB1 MMP7 CLIC1 0.885

 67

 MMP7 YWHAG PLA2G7 0.885

 68

 CHRDL1 CMA1 CLIC1 0.885

 69

 STX1A YWHAG L1CAM 0.885

 70

 MMP7 CMA1 NME2 0.885

 71

 BMP1 MMP12 CLIC1 0.884

 72

 C9 CHRDL1 YWHAG 0.884

 73

 KLK3-SERPINA3 CMA1 CLICK1 0.884

 74

 EGFR MMP7 CLIC1 0.884

 75

 STX1A YWHAG CLICK1 0.884

 76

 MMP7 AHSG YWHAG 0.884

 77

 IGFBP2 MMP7 CLIC1 0.884

 78

 MMP7 TPT1 YWHAG 0.884

 79

 KLK3-SERPINA3 COL18A1 CLICK1 0.884

 80

 EGFR MMP7 YWHAG 0.884

 81

 C9 YWHAG L1CAM 0.884

 82

 KLK3-SERPINA3 MMP7 TPI1 0.884

 83

 KLK3-SERPINA3 BDNF CLICK1 0.884

 84

 MMP7 CA6 YWHAG 0.884

 85

 BMP1 YWHAG CRP 0.883

 86

 MMP7 CMA1 TPI1 0.883

 87

 KLK3-SERPINA3 MMP7 NME2 0.883

 88

 BDNF C9 YWHAG 0.883

 89

 AHSG STX1A YWHAG 0.883

 90

 C9 MMP12 CLIC1 0.883

 91

 C9 BMP1 YWHAG 0.883

 92

 KLK3-SERPINA3 STX1A TPT1 0.883

 93

 CNTN1 C9 YWHAG 0.883

 94

 C9 CA6 YWHAG 0.883

 95

 CA6 STX1A YWHAG 0.883

 96

 MMP7 CNTN1 YWHAG 0.883

 97

 KLK3-SERPINA3 STX1A NME2 0.883

 98

 MMP7 HNRNPAB CLICK1 0.883

 99

 MMP7 SERPINA1 YWHAG 0.883

 100

 TGFBI CMA1 YWHAG 0.883

Table 5: 4 biomarker panels

 Markers

 VC ABC

 one

 KLK3-SERPINA3 MMP7 STX1A CLICK1 0.911

 2

 KLK3-SERPINA3 BDNF STX1A CLICK1 0.910

 3

 BDNF C9 CHRDL1 CLICK1 0.909

 4

 BDNF C9 STX1A CLICK1 0.908

 5

 MMP7 C9 YWHAG TPI1 0.908

 6

 MMP7 C9 YWHAG CLICK1 0.908

 7

 MMP7 GHR STX1A CLICK1 0.907

 8

 KLK3-SERPINA3 MMP7 CMA1 CLICK1 0.907

 9

 KLK3-SERPINA3 BDNF MMP7 CLICK1 0.907

 10

 MMP7 C9 CMA1 CLICK1 0.907

 eleven

 BDNF MMP7 YWHAG CLICK1 0.907

 12

 CDH1 MMP7 C9 YWHAG 0.907

 13

 KLK3-SERPINA3 MMP7 LRIG3 CLICK1 0.906

 14

 MMP7 GHR CMA1 CLICK1 0.906

 fifteen

 MMP7 C9 YWHAG NME2 0.906

 16

 CDH1 MMP7 STX1A YWHAG 0.906

 17

 MMP7 C9 LRIG3 YWHAG 0.905

 18

 MMP7 C9 YWHAG GPI 0.905

 19

 CDH1 MMP7 STX1A CLICK1 0.905

 twenty

 BDNF MMP7 GHR CLICK1 0.905

 twenty-one

 MMP7 STX1A YWHAG CLICK1 0.905

 22

 BDNF MMP7 LRIG3 CLICK1 0.905

 2. 3

 BDNF MMP7 STX1A CLICK1 0.905

 24

 MMP7 LRIG3 YWHAG CLICK1 0.905

 25

 BDNF MMP7 CMA1 CLICK1 0.905

 26

 MMP7 C9 TGFBI YWHAG 0.904

 27

 CDH1 MMP7 LRIG3 YWHAG 0.904

 28

 KLK3-SERPINA3 CHRDL1 CMA1 CLICK1 0.904

 29

 TGFBI STX1A YWHAG CRP 0.904

 30

 BDNF MMP7 C9 CLIC1 0.904

 31

 KLK3-SERPINA3 CHRDL1 STX1A CLICK1 0.904

 32

 KLK3-SERPINA3 MMP7 STX1A YWHAG 0.904

 33

 KLK3-SERPINA3 BMP1 STX1A CLICK1 0.904

 3. 4

 MMP7 STX1A YWHAG NME2 0.904

 35

 BDNF MMP7 TGFBI CLIC1 0.904

 36

 MMP7 C9 YWHAG L1CAM 0.904

 37

 MMP7 TGFBI LRIG3 YWHAG 0.904

 38

 KLK3-SERPINA3 BDNF CHRDL1 CLICK1 0.904

 39

 KLK3-SERPINA3 GHR STX1A CLICK1 0.904

 40

 KLK3-SERPINA3 LRIG3 CHRDL1 CLICK1 0.904

 41

 KLK3-SERPINA3 MMP7 LRIG3 YWHAG 0.904

 42

 KLK3-SERPINA3 LRIG3 STX1A CLICK1 0.904

 43

 MMP7 GHR BMP1 CLICK1 0.904

 44

 CDH1 MMP7 CMA1 CLIC1 0.904

 Four. Five

 LRIG3 STX1A YWHAG CRP 0.904

 46

 MMP7 GHR YWHAG CLICK1 0.904

 47

 BDNF GHR STX1A CLICK1 0.904

 48

 MMP7 C9 CMA1 YWHAG 0.904

 49

 MMP7 LRIG3 GPI CLICK1 0.904

 fifty

 MMP7 C9 STX1A YWHAG 0.903

 51

 BDNF MMP7 GPI CLIC1 0.903

 52

 KLK3-SERPINA3 MMP7 YWHAG CLICK1 0.903

 53

 MMP7 TGFBI STX1A YWHAG 0.903

 54

 KLK3-SERPINA3 COL18A1 STX1A CLICK1 0.903

 55

 MMP7 TGFBI CMA1 CLICK1 0.903

 56

 MMP7 C9 YWHAG PA2G4 0.903

 57

 MMP7 C9 YWHAG AKR7A2 0.903

 58

 KLK3-SERPINA3 MMP7 BMP1 CLICK1 0.903

 59

 MMP7 GHR LRIG3 CLICK1 0.903

 60

 MMP7 GHR C9 CLICK1 0.903

 61

 MMP7 BMP1 YWHAG CLICK1 0.903

 62

 KLK3-SERPINA3 MMP7 GHR CLICK1 0.903

 63

 BDNF STX1A MMP12 CLICK1 0.903

 64

 MMP7 LRIG3 YWHAG CRP 0.903

 65

 BDNF IGFBP2 MMP7 CLICK1 0.903

 66

 GHR STX1A CRP CLIC1 0.903

 67

 BDNF STX1A CRP CLIC1 0.902

 68

 KLK3-SERPINA3 CNTN1 BMP1 CLICK1 0.902

 69

 BDNF MMP7 C9 YWHAG 0.902

 70

 CDH1 MMP7 TGFBI YWHAG 0.902

 71

 BDNF IGFBP2 STX1A CLICK1 0.902

 72

 KLK3-SERPINA3 MMP7 NME2 CLICK1 0.902

 73

 KLK3-SERPINA3 MMP7 TPI1 CLICK1 0.902

 74

 MMP7 LRIG3 YWHAG NME2 0.902

 75

 KLK3-SERPINA3 EGFR STX1A CLICK1 0.902

 76

 BDNF IGFBP2 LRIG3 CLICK1 0.902

 77

 MMP7 CMA1 YWHAG CLICK1 0.902

 78

 MMP7 GHR STX1A YWHAG 0.902

 79

 HMGB1 MMP7 C9 YWHAG 0.902

 80

 IGFBP2 MMP7 CMA1 CLICK1 0.902

 81

 MMP7 GHR GPI CLIC1 0.902

 82

 KLK3-SERPINA3 STX1A YWHAG CLICK1 0.902

 83

 KLK3-SERPINA3 SERPINA1 STX1A YWHAG 0.902

 84

 BDNF PLAUR LRIG3 CLICK1 0.902

 85

 BDNF TGFBI STX1A CLICK1 0.902

 86

 BDNF MMP7 ITIH4 CLICK1 0.902

 87

 MMP7 LRIG3 YWHAG GPI 0.902

 88

 MMP7 BMP1 YWHAG GPI 0.902

 89

 C9 CHRDL1 CMA1 CLICK1 0.902

 90

 MMP7 BMP1 CMA1 CLICK1 0.902

 91

 KLK3-SERPINA3 MMP7 CNTN1 CLICK1 0.902

 92

 MMP7 CMA1 HNRNPAB CLICK1 0.902

 93

 KLK3-SERPINA3 LRIG3 STX1A YWHAG 0.902

 94

 BDNF LRIG3 STX1A CLICK1 0.902

 95

 MMP7 TGFBI CMA1 YWHAG 0.902

 96

 MMP7 LRIG3 YWHAG TPI1 0.902

 97

 MMP7 CMA1 NME2 CLICK1 0.902

 98

 MMP7 GHR CRP CLIC1 0.902

 99

 C9 LRIG3 CHRDL1 CLICK1 0.902

 100

 MMP7 LRIG3 STX1A CLICK1 0.902

Table 6: 5 biomarker panels

 Markers

 VC ABC

 one

 TGFBI LRIG3 CHRDL1 NME2 CRP 0.922

 2

 KLK3-SERPINA3 BDNF MMP7 STX1A CLICK1 0.920

 3

 BDNF MMP7 GHR STX1A CLICK1 0.919

 4

 BDNF MMP7 C9 YWHAG CLICK1 0.918

 5

 KLK3-SERPINA3 MMP7 GHR STX1A CLICK1 0.918

 6

 BDNF C9 CHRDL1 AHSG CLICK1 0.918

 7

 CDH1 MMP7 GHR STX1A CLICK1 0.918

 8

 KLK3-SERPINA3 MMP7 STX1A NME2 CLICK1 0.918

 9

 MMP7 GHR STX1A YWHAG CLICK1 0.918

 10

 MMP7 GHR STX1A GPI CLICK1 0.918

 eleven

 KLK3-SERPINA3 MMP7 LRIG3 STX1A CLICK1 0.917

 12

 BDNF MMP7 GHR GPI CLICK1 0.917

 13

 BDNF MMP7 STX1A YWHAG CLICK1 0.917

 14

 BDNF TGFBI LRIG3 CHRDL1 CLICK1 0.917

 fifteen

 KLK3-SERPINA3 BDNF LRIG3 STX1A CLICK1 0.917

 16

 KLK3-SERPINA3 BDNF C9 STX1A CLICK1 0.917

 17

 BDNF MMP7 LRIG3 YWHAG CLICK1 0.917

 18

 BDNF GHR C9 STX1A CLICK1 0.916

 19

 BDNF IGFBP2 LRIG3 CRP CLIC1 0.916

 twenty

 KLK3-SERPINA3 BDNF CHRDL1 STX1A CLICK1 0.916

 twenty-one

 KLK3-SERPINA3 CDH1 MMP7 STX1A CLICK1 0.916

 22

 MMP7 GHR STX1A CRP CLICK1 0.916

 2. 3

 BDNF MMP7 TGFBI STX1A CLICK1 0.916

 24

 MMP7 GHR TGFBI STX1A CLICK1 0.916

 25

 MMP7 GHR C9 STX1A CLICK1 0.916

 26

 BDNF MMP7 GHR TGFBI CLICK1 0.916

 27

 MMP7 GHR STX1A NME2 CLICK1 0.916

 28

 KLK3-SERPINA3 HMGB1 MMP7 STX1A CLICK1 0.916

 29

 MMP7 C9 STX1A YWHAG NME2 0.916

 30

 BDNF MMP7 LRIG3 STX1A CLICK1 0.916

 31

 MMP7 C9 STX1A YWHAG CLICK1 0.916

 32

 BDNF CDH1 MMP7 STX1A CLICK1 0.916

 33

 BDNF C9 TGFBI CHRDL1 CLICK1 0.915

 3. 4

 MMP7 C9 LRIG3 YWHAG TPI1 0.915

 35

 KLK3-SERPINA3 BDNF MMP7 LRIG3 CLICK1 0.915

 36

 BDNF C9 LRIG3 CHRDL1 CLICK1 0.915

 37

 KLK3-SERPINA3 BDNF MMP7 CMA1 CLICK1 0.915

 38

 BDNF LRIG3 CHRDL1 CRP CLICK1 0.915

 39

 BDNF MMP7 STX1A ITIH4 CLICK1 0.915

 40

 BDNF MMP7 GHR C9 CLICK1 0.915

 41

 BDNF MMP7 C9 GPI CLICK1 0.915

 42

 HMGB1 MMP7 GHR STX1A CLICK1 0.915

 43

 BDNF MMP7 LRIG3 GPI CLICK1 0.915

 44

 GHR BMP1 STX1A CRP CLICK1 0.915

 Four. Five

 BDNF MMP7 BMP1 GPI CLICK1 0.915

 46

 KLK3-SERPINA3 MMP7 STX1A YWHAG CLICK1 0.915

 47

 KLK3-SERPINA3 CNTN1 BMP1 CHRDL1 CLICK1 0.915

 48

 BDNF GHR STX1A CRP CLICK1 0.915

 49

 KLK3-SERPINA3 BDNF TGFBI STX1A CLICK1 0.915

 fifty

 KLK3-SERPINA3 BDNF MMP7 PA2G4 CLICK1 0.915

 51

 CDH1 MMP7 TGFBI STX1A YWHAG 0.915

 52

 BDNF MMP7 C9 STX1A CLICK1 0.915

 53

 MMP7 GHR TGFBI CMA1 CLICK1 0.915

 54

 BDNF MMP7 TGFBI CMA1 CLICK1 0.915

 55

 CDH1 MMP7 C9 TGFBI YWHAG 0.915

 56

 MMP7 C9 LRIG3 YWHAG NME2 0.915

 57

 BDNF MMP7 STX1A NME2 CLICK1 0.915

 58

 BDNF EGFR TGFBI STX1A CLICK1 0.915

 59

 KLK3-SERPINA3 MMP7 LRIG3 GPI CLICK1 0.915

 60

 BDNF MMP7 STX1A GPI CLICK1 0.915

 61

 MMP7 C9 LRIG3 YWHAG GPI 0.915

 62

 KLK3-SERPINA3 MMP7 CMA1 TPI1 CLICK1 0.915

 63

 CDH1 MMP7 C9 STX1A YWHAG 0.915

 64

 KLK3-SERPINA3 BDNF CNTN1 CHRDL1 CLICK1 0.915

 65

 KLK3-SERPINA3 BDNF LRIG3 CHRDL1 CLICK1 0.915

 66

 BDNF MMP7 GHR LRIG3 CLICK1 0.914

 67

 KLK3-SERPINA3 BDNF MMP7 NME2 CLICK1 0.914

 68

 BDNF IGFBP2 MMP7 GPI CLICK1 0.914

 69

 KLK3-SERPINA3 BDNF STX1A CLICK1 PLA2G7 0.914

 70

 CDH1 MMP7 GHR CMA1 CLICK1 0.914

 71

 MMP7 C9 LRIG3 GPI CLICK1 0.914

 72

 MMP7 GHR STX1A PA2G4 CLICK1 0.914

 73

 KLK3-SERPINA3 MMP7 STX1A PA2G4 CLICK1 0.914

 74

 KLK3-SERPINA3 MMP7 STX1A TPI1 CLICK1 0.914

 75

 KLK3-SERPINA3 MMP7 STX1A HNRNPAB CLICK1 0.914

 76

 MMP7 GHR LRIG3 GPI CLICK1 0.914

 77

 MMP7 GHR CMA1 GPI CLICK1 0.914

 78

 BDNF IGFBP2 MMP7 LRIG3 CLICK1 0.914

 79

 KLK3-SERPINA3 BDNF MMP7 TPI1 CLICK1 0.914

 80

 BDNF MMP7 STX1A TPT1 CLICK1 0.914

 81

 BDNF LRIG3 STX1A CRP CLICK1 0.914

 82

 BDNF MMP7 STX1A CLICK1 PLA2G7 0.914

 83

 KLK3-SERPINA3 BDNF AHSG STX1A CLICK1 0.914

 84

 KLK3-SERPINA3 MMP7 CNTN1 STX1A CLICK1 0.914

 85

 BDNF GHR TGFBI STX1A CLICK1 0.914

 86

 BDNF MMP7 NME2 ITIH4 CLICK1 0.914

 87

 KLK3-SERPINA3 CNTN1 BMP1 STX1A CLICK1 0.914

 88

 MMP7 C9 CMA1 NME2 CLICK1 0.914

 89

 BDNF MMP7 LRIG3 NME2 CLICK1 0.914

 90

 BDNF TGFBI LRIG3 STX1A CLICK1 0.914

 91

 KLK3-SERPINA3 CDH1 MMP7 STX1A YWHAG 0.914

 92

 MMP7 C9 LRIG3 YWHAG CLICK1 0.914

 93

 BDNF MMP7 TGFBI LRIG3 CLICK1 0.914

 94

 KLK3-SERPINA3 BDNF STX1A CRP CLICK1 0.914

 95

 BDNF MMP7 BMP1 YWHAG CLICK1 0.914

 96

 KLK3-SERPINA3 MMP7 LRIG3 CMA1 CLICK1 0.914

 97

 KLK3-SERPINA3 MMP7 BMP1 STX1A CLICK1 0.914

 98

 BDNF IGFBP2 MMP7 STX1A CLICK1 0.914

 99

 KLK3-SERPINA3 MMP7 STX1A YWHAG GPI 0.914

 100

 MMP7 LRIG3 STX1A YWHAG CLICK1 0.914

Table 7: 6 biomarker panels

 Markers

 VC ABC

 one

 BDNF CLIC1 MMP7 GHR STX1A GPI 0.928

 2

 BDNF CLIC1 TGFBI LRIG3 CHRDL1 CRP 0.928

 3

 KLK3-SERPINA3 CLICK1 BDNF MMP7 STX1A NME2 0.928

 4

 KLK3-SERPINA3 CLICK1 BDNF MMP7 GHR STX1A 0.927

 5

 BDNF CLIC1 MMP7 GHR TGFBI STX1A 0.927

 6

 TGFBI CRP LRIG3 CHRDL1 AHSG NME2 0.927

 7

 KLK3-SERPINA3 CLICK1 BDNF MMP7 TGFBI STX1A 0.927

 8

 BDNF CLIC1 MMP7 C9 STX1A YWHAG 0.926

 9

 KLK3-SERPINA3 CLICK1 BDNF MMP7 STX1A TPT1 0.926

 10

 BDNF CLIC1 MMP7 GHR STX1A PA2G4 0.926

 eleven

 KLK3-SERPINA3 CLICK1 BDNF MMP7 LRIG3 STX1A 0.925

 12

 BDNF CLIC1 MMP7 C9 LRIG3 YWHAG 0.925

 13

 KLK3-SERPINA3 CLICK1 MMP7 GHR STX1A TPI1 0.925

 14

 KLK3-SERPINA3 CLICK1 BDNF MMP7 KIT STX1A 0.925

 fifteen

 KLK3-SERPINA3 CLICK1 BDNF MMP7 STX1A PA2G4 0.925

 16

 BDNF CLIC1 MMP7 GHR STX1A NME2 0.925

 17

 BDNF CLIC1 IGFBP2 MMP7 LRIG3 NME2 0.925

 18

 BDNF CLIC1 GHR C9 AHSG STX1A 0.925

 19

 KLK3-SERPINA3 CLICK1 BDNF MMP7 STX1A TPI1 0.925

 twenty

 BDNF CLIC1 MMP7 GHR C9 STX1A 0.925

 twenty-one

 BDNF CLIC1 MMP7 GHR STX1A CRP 0.925

 22

 BDNF CLIC1 MMP7 GHR LRIG3 GPI 0.925

 2. 3

 KLK3-SERPINA3 CLICK1 BDNF CDH1 MMP7 STX1A 0.925

 24

 MMP7 CLIC1 GHR C9 STX1A YWHAG 0.925

 25

 MMP7 CLIC1 GHR C9 STX1A HNRNPAB 0.925

 26

 KLK3-SERPINA3 CLICK1 BDNF TGFBI CHRDL1 STX1A 0.925

 27

 KLK3-SERPINA3 PLA2G7 BDNF MMP7 STX1A CLICK1 0.925

 28

 MMP7 CLIC1 GHR C9 STX1A GPI 0.925

 29

 BDNF CLIC1 MMP7 GHR LRIG3 YWHAG 0.925

 30

 KLK3-SERPINA3 CLICK1 MMP7 GHR STX1A NME2 0.925

 31

 BDNF PLA2G7 MMP7 GHR STX1A CLICK1 0.925

 32

 BDNF CLIC1 MMP7 GHR STX1A TPT1 0.925

 33

 BDNF CLIC1 MMP7 C9 STX1A NME2 0.924

 3. 4

 KLK3-SERPINA3 CLICK1 BDNF MMP7 LRIG3 NME2 0.924

 35

 BDNF CLIC1 MMP7 LRIG3 STX1A GPI 0.924

 36

 BDNF CLIC1 MMP7 GHR AHSG STX1A 0.924

 37

 BDNF CLIC1 MMP7 GHR C9 YWHAG 0.924

 38

 CDH1 CLIC1 MMP7 GHR STX1A CRP 0.924

 39

 BDNF CLIC1 IGFBP2 MMP7 LRIG3 GPI 0.924

 40

 KLK3-SERPINA3 CLICK1 BDNF MMP7 STX1A YWHAG 0.924

 41

 KLK3-SERPINA3 CLICK1 BDNF MMP7 STX1A GPI 0.924

 42

 BDNF CLIC1 CDH1 MMP7 GHR STX1A 0.924

 43

 BDNF CLIC1 IGFBP2 MMP7 TPI1 ITIH4 0.924

 44

 BDNF CLIC1 MMP7 STX1A NME2 ITIH4 0.924

 Four. Five

 BDNF CLIC1 MMP7 GHR STX1A YWHAG 0.924

 46

 KLK3-SERPINA3 CLICK1 BDNF CNTN1 TGFBI CHRDL1 0.924

 47

 KLK3-SERPINA3 CLICK1 CDH1 MMP7 LRIG3 STX1A 0.924

 48

 KLK3-SERPINA3 CLICK1 MMP7 LRIG3 STX1A NME2 0.924

 49

 KLK3-SERPINA3 CLICK1 BDNF TGFBI LRIG3 STX1A 0.923

 fifty

 BDNF CLIC1 MMP7 TGFBI LRIG3 GPI 0.923

 51

 BDNF CLIC1 TGFBI LRIG3 STX1A CRP 0.923

 52

 KLK3-SERPINA3 CLICK1 CDH1 MMP7 GHR STX1A 0.923

 53

 BDNF CLIC1 MMP7 GHR TGFBI GPI 0.923

 54

 BDNF CLIC1 MMP7 C9 CMA1 NME2 0.923

 55

 KLK3-SERPINA3 CLICK1 BDNF MMP7 AHSG STX1A 0.923

 56

 KLK3-SERPINA3 CLICK1 MMP7 GHR STX1A GPI 0.923

 57

 BDNF CLIC1 MMP7 C9 STX1A TPT1 0.923

 58

 BDNF CLIC1 MMP7 GHR CNTN1 TGFBI 0.923

 59

 MMP7 CLIC1 GHR TGFBI STX1A CRP 0.923

 60

 KLK3-SERPINA3 CLICK1 MMP7 GHR STX1A YWHAG 0.923

 61

 TGFBI CRP LRIG3 CHRDL1 STX1A NME2 0.923

 62

 BDNF CLIC1 MMP7 C9 STX1A GPI 0.923

 63

 BDNF CLIC1 IGFBP2 MMP7 TGFBI STX1A 0.923

 64

 KLK3-SERPINA3 CLICK1 BDNF MMP7 STX1A HNRNPAB 0.923

 65

 MMP7 CLIC1 GHR C9 STX1A NME2 0.923

 66

 CDH1 CLIC1 MMP7 GHR TGFBI STX1A 0.923

 67

 KLK3-SERPINA3 CLICK1 MMP7 GHR STX1A PA2G4 0.923

 68

 BDNF CLIC1 MMP7 TGFBI STX1A GPI 0.923

 69

 BDNF CLIC1 MMP7 STX1A YWHAG ITIH4 0.923

 70

 BDNF CLIC1 MMP7 GHR LRIG3 STX1A 0.923

 71

 BDNF CLIC1 MMP7 KIT GHR STX1A 0.923

 72

 MMP7 CLIC1 GHR TGFBI STX1A GPI 0.923

 73

 BDNF CLIC1 MMP7 STX1A TPI1 ITIH4 0.923

 74

 BDNF CLIC1 MMP7 TGFBI LRIG3 STX1A 0.923

 75

 BDNF CLIC1 EGFR TGFBI AHSG STX1A 0.923

 76

 KLK3-SERPINA3 CLICK1 BDNF TGFBI LRIG3 CHRDL1 0.923

 77

 CDH1 CLIC1 MMP7 GHR STX1A GPI 0.923

 78

 BDNF CLIC1 IGFBP2 MMP7 LRIG3 TPI1 0.923

 79

 BDNF CLIC1 GHR LRIG3 STX1A CRP 0.923

 80

 BDNF CLIC1 CDH1 MMP7 LRIG3 STX1A 0.923

 81

 KLK3-SERPINA3 CLICK1 MMP7 GHR TGFBI STX1A 0.923

 82

 BDNF CLIC1 IGFBP2 LRIG3 AHSG CRP 0.923

 83

 KLK3-SERPINA3 CLICK1 BDNF MMP7 LRIG3 GPI 0.923

 84

 BDNF CLIC1 MMP7 GHR LRIG3 NME2 0.923

 85

 KLK3-SERPINA3 CLICK1 BDNF EGFR TGFBI STX1A 0.923

 86

 BDNF CLIC1 MMP7 GHR TGFBI LRIG3 0.923

 87

 MMP7 CLIC1 GHR C9 CMA1 NME2 0.923

 88

 BDNF CLIC1 MMP7 GHR TGFBI CMA1 0.923

 89

 MMP7 CLIC1 GHR STX1A NME2 CRP 0.922

 90

 BDNF CLIC1 MMP7 C9 LRIG3 GPI 0.922

 91

 KLK3-SERPINA3 CLICK1 BDNF MMP7 LRIG3 TPT1 0.922

 92

 BDNF CLIC1 MMP7 STX1A TPT1 ITIH4 0.922

 93

 KIT TPI1 MMP7 C9 LRIG3 YWHAG 0.922

 94

 BDNF CLIC1 CDH1 MMP7 STX1A ITIH4 0.922

 95

 MMP7 CLIC1 GHR STX1A TPI1 CRP 0.922

 96

 BDNF CLIC1 C9 TGFBI LRIG3 CHRDL1 0.922

 97

 KLK3-SERPINA3 CLICK1 BDNF CNTN1 BMP1 CHRDL1 0.922

 98

 BDNF CLIC1 GHR TGFBI STX1A CRP 0.922

 99

 KLK3-SERPINA3 CLICK1 LRIG3 CHRDL1 STX1A CRP 0.922

 100

 MMP7 GHR LRIG3 STX1A YWHAG 0.922

 CLICK1

Table 8: 7 biomarker panels

 Markers

 VC ABC

 one

 BDNF GPI MMP7 CLIC1 GHR TGFBI STX1A 0.933

 2

 KLK3-SERPINA3 NME2 BDNF CLICK1 MMP7 GHR STX1A 0.932

 3

 BDNF GPI MMP7 CLIC1 GHR C9 STX1A 0.932

 4

 KLK3-SERPINA3 PA2G4 BDNF CLICK1 MMP7 GHR STX1A 0.932

 5

 BDNF CRP MMP7 CLIC1 GHR TGFBI STX1A 0.932

 6

 BDNF PA2G4 MMP7 CLIC1 GHR TGFBI STX1A 0.932

 7

 KLK3-SERPINA3 TPI1 BDNF CLICK1 MMP7 GHR STX1A 0.932

 8

 BDNF TPT1 MMP7 CLIC1 GHR C9 STX1A 0.932

 9

 KLK3-SERPINA3 ITIH4 BDNF CLICK1 MMP7 STX1A NME2 0.932

 10

 BDNF STX1A CDH1 CLICK1 MMP7 GHR TGFBI 0.932

 eleven

 BDNF CRP TGFBI CLIC1 LRIG3 CHRDL1 STX1A 0.932

 12

 BDNF NME2 MMP7 CLIC1 GHR TGFBI STX1A 0.932

 13

 KLK3-SERPINA3 NME2 BDNF CLICK1 MMP7 LRIG3 STX1A 0.932

 14

 KLK3-SERPINA3 GPI BDNF CLICK1 MMP7 LRIG3 STX1A 0.932

 fifteen

 KLK3-SERPINA3 STX1A BDNF CLICK1 KIT MMP7 GHR 0.931

 16

 KLK3-SERPINA3 CHRDL1 BDNF CLICK1 CNTN1 TGFBI LRIG3 0.931

 17

 BDNF NME2 MMP7 CLIC1 GHR C9 STX1A 0.931

 18

 KLK3-SERPINA3 TPT1 BDNF CLICK1 MMP7 LRIG3 STX1A 0.931

 19

 KLK3-SERPINA3 STX1A BDNF CLICK1 MMP7 TGFBI LRIG3 0.931

 twenty

 KLK3-SERPINA3 GPI BDNF CLICK1 MMP7 GHR STX1A 0.931

 twenty-one

 BDNF YWHAG MMP7 CLIC1 GHR C9 STX1A 0.931

 22

 BDNF ITIH4 MMP7 CLIC1 GHR STX1A NME2 0.931

 2. 3

 BDNF STX1A MMP7 CLIC1 GHR TGFBI LRIG3 0.931

 24

 KLK3-SERPINA3 TPT1 BDNF CLICK1 MMP7 GHR STX1A 0.931

 25

 BDNF GPI MMP7 CLIC1 GHR AHSG STX1A 0.931

 26

 KLK3-SERPINA3 ITIH4 BDNF CLICK1 MMP7 STX1A TPI1 0.931

 27

 BDNF GPI MMP7 CLIC1 GHR STX1A PA2G4 0.931

 28

 KLK3-SERPINA3 PA2G4 BDNF CLICK1 MMP7 KIT STX1A 0.931

 29

 BDNF CRP MMP7 CLIC1 GHR STX1A NME2 0.931

 30

 KLK3-SERPINA3 NME2 BDNF CLICK1 MMP7 TGFBI STX1A 0.931

 31

 BDNF STX1A MMP7 CLICK1 GHR TGFBI AHSG 0.931

 32

 BDNF STX1A CDH1 CLICK1 MMP7 GHR AHSG 0.931

 33

 KLK3-SERPINA3 NME2 BDNF CLIC1 EGFR MMP7 STX1A 0.931

 3. 4

 KLK3-SERPINA3 STX1A BDNF CLICK1 MMP7 KIT LRIG3 0.931

 35

 BDNF GPI MMP7 CLIC1 GHR LRIG3 STX1A 0.930

 36

 BDNF CRP GHR CLIC1 TGFBI LRIG3 CHRDL1 0.930

 37

 BDNF CLIC1 MMP7 PLA2G7 GHR TGFBI STX1A 0.930

 38

 BDNF YWHAG MMP7 CLICK1 GHR C9 LRIG3 0.930

 39

 BDNF TPT1 CLIC1 KIT MMP7 GHR STX1A 0.930

 40

 BDNF ITIH4 MMP7 CLIC1 C9 STX1A NME2 0.930

 41

 KLK3-SERPINA3 STX1A BDNF CLICK1 TGFBI LRIG3 CHRDL1 0.930

 42

 KLK3-SERPINA3 TPI1 BDNF CLIC1 MMP7 KIT STX1A 0.930

 43

 BDNF GPI MMP7 CLIC1 TGFBI LRIG3 STX1A 0.930

 44

 BDNF CRP MMP7 CLIC1 GHR STX1A GPI 0.930

 Four. Five

 BDNF GPI MMP7 CLIC1 GHR TGFBI LRIG3 0.930

 46

 BDNF PA2G4 MMP7 CLIC1 GHR C9 STX1A 0.930

 47

 BDNF TPT1 MMP7 CLIC1 GHR CHRDL1 STX1A 0.930

 48

 KLK3-SERPINA3 PA2G4 BDNF CLICK1 MMP7 CHRDL1 STX1A 0.930

 49

 BDNF PA2G4 MMP7 CLICK1 GHR AHSG STX1A 0.930

 fifty

 KLK3-SERPINA3 NME2 BDNF CLICK1 IGFBP2 MMP7 STX1A 0.930

 51

 KLK3-SERPINA3 STX1A BDNF CLICK1 CDH1 MMP7 KIT 0.930

 52

 KLK3-SERPINA3 STX1A BDNF CLICK1 CDH1 MMP7 LRIG3 0.930

 53

 BDNF CRP GHR CLIC1 TGFBI LRIG3 STX1A 0.930

 54

 KLK3-SERPINA3 PA2G4 BDNF CLICK1 MMP7 TGFBI STX1A 0.930

 55

 KLK3-SERPINA3 NME2 BDNF CLICK1 MMP7 KIT LRIG3 0.930

 56

 KLK3-SERPINA3 STX1A BDNF CLICK1 MMP7 GHR TGFBI 0.930

 57

 KLK3-SERPINA3 NME2 BDNF CLIC1 MMP7 KIT STX1A 0.930

 58

 BDNF TPI1 MMP7 CLIC1 GHR SERPINA1 STX1A 0.930

 59

 BDNF STX1A EGFR CLIC1 MMP7 GHR TGFBI 0.930

 60

 BDNF CRP GHR CLIC1 TGFBI CHRDL1 STX1A 0.930

 61

 BDNF CLIC1 MMP7 PLA2G7 GHR STX1A CRP 0.930

 62

 BDNF CRP MMP7 CLIC1 GHR STX1A TPT1 0.930

 63

 BDNF STX1A CLIC1 KIT MMP7 GHR TGFBI 0.930

 64

 BDNF PA2G4 KIT CLIC1 MMP7 GHR STX1A 0.930

 65

 KLK3-SERPINA3 CLICK1 BDNF PLA2G7 MMP7 GHR STX1A 0.930

 66

 KLK3-SERPINA3 NME2 BDNF CLICK1 IGFBP2 MMP7 LRIG3 0.930

 67

 BDNF NME2 CRP KIT TGFBI LRIG3 CHRDL1 0.930

 68

 TGFBI NME2 LRIG3 CRP CHRDL1 AHSG STX1A 0.930

 69

 BDNF TPI1 MMP7 CLIC1 GHR TGFBI STX1A 0.929

 70

 KLK3-SERPINA3 CLICK1 BDNF PLA2G7 MMP7 STX1A NME2 0.929

 71

 KLK3-SERPINA3 PA2G4 KIT CLIC1 MMP7 GHR STX1A 0.929

 72

 KLK3-SERPINA3 TPI1 BDNF CLICK1 MMP7 KIT LRIG3 0.929

 73

 BDNF GPI CDH1 CLIC1 MMP7 GHR STX1A 0.929

 74

 BDNF TPT1 MMP7 CLIC1 GHR TGFBI STX1A 0.929

 75

 BDNF CRP MMP7 CLIC1 GHR LRIG3 GPI 0.929

 76

 KLK3-SERPINA3 NME2 BDNF CLICK1 MMP7 C9 STX1A 0.929

 77

 BDNF NME2 MMP7 CLICK1 C9 TGFBI CMA1 0.929

 78

 CDH1 CRP MMP7 CLIC1 GHR TGFBI STX1A 0.929

 79

 BDNF HNRNPAB MMP7 CLICK1 GHR C9 STX1A 0.929

 80

 BDNF YWHAG MMP7 CLICK1 C9 LRIG3 STX1A 0.929

 81

 BDNF CRP IGFBP2 CLIC1 TGFBI LRIG3 STX1A 0.929

 82

 BDNF NME2 MMP7 CLIC1 GHR LRIG3 STX1A 0.929

 83

 BDNF CRP IGFBP2 CLICK1 MMP7 LRIG3 NME2 0.929

 84

 BDNF PA2G4 MMP7 CLIC1 GHR CHRDL1 STX1A 0.929

 85

 BDNF STX1A MMP7 CLIC1 GHR C9 TGFBI 0.929

 86

 BDNF NME2 MMP7 CLIC1 C9 TGFBI STX1A 0.929

 87

 BDNF ITIH4 EGFR CLIC1 MMP7 STX1A TPI1 0.929

 88

 KLK3-SERPINA3 YWHAG BDNF CLICK1 MMP7 C9 STX1A 0.929

 89

 KLK3-SERPINA3 PA2G4 BDNF CLICK1 MMP7 LRIG3 STX1A 0.929

 90

 KLK3-SERPINA3 ITIH4 BDNF CLICK1 MMP7 STX1A PA2G4 0.929

 91

 KLK3-SERPINA3 HNRNPAB BDNF CLICK1 MMP7 LRIG3 STX1A 0.929

 92

 KLK3-SERPINA3 STX1A BDNF CLIC1 EGFR MMP7 TGFBI 0.929

 93

 BDNF ITIH4 MMP7 CLIC1 GHR STX1A TPI1 0.929

 94

 BDNF CRP CDH1 CLIC1 MMP7 GHR STX1A 0.929

 95

 BDNF CLIC1 MMP7 PLA2G7 GHR STX1A NME2 0.929

 96

 KLK3-SERPINA3 TPI1 BDNF CLICK1 MMP7 TGFBI STX1A 0.929

 97

 BDNF ITIH4 MMP7 CLIC1 GHR STX1A PA2G4 0.929

 98

 MMP7 CRP GHR CLIC1 BMP1 STX1A NME2 0.929

 99

 KLK3-SERPINA3 CLICK1 BDNF PLA2G7 MMP7 STX1A L1CAM 0.929

 100

 BDNF GPI KIT CLIC1 MMP7 GHR STX1A 0.929

Table 9: 8 biomarker panels

 Markers

 VC ABC

 one

 KLK3-SERPINA3 STX1A BDNF PA2G4 KIT CLIC1 MMP7 GHR 0.940

 2

 BDNF STX1A TGFBI CRP LRIG3 CLICK1 CHRDL1 AHSG 0.938

 3

 BDNF NME2 MMP7 CRP GHR CLICK1 TGFBI STX1A 0.938

 4

 KLK3-SERPINA3 STX1A BDNF NME2 CLICK KIT1 MMP7 LRIG3 0.937

 5

 BDNF STX1A MMP7 NME2 GHR CLICK1 TGFBI LRIG3 0.937

 6

 KLK3-SERPINA3 STX1A BDNF NME2 MMP7 CLICK1 GHR TGFBI 0.937

 7

 KLK3-SERPINA3 STX1A BDNF TPT1 CLICK KIT1 MMP7 GHR 0.937

 8

 KLK3-SERPINA3 STX1A BDNF NME2 MMP7 CLICK1 GHR LRIG3 0.937

 9

 BDNF STX1A MMP7 GPI GHR CLICK1 TGFBI LRIG3 0.937

 10

 BDNF GPI MMP7 CRP GHR CLICK1 TGFBI STX1A 0.936

 eleven

 KLK3-SERPINA3 STX1A BDNF TPI1 CLICK KIT1 MMP7 GHR 0.936

 12

 BDNF STX1A EGFR NME2 MMP7 CLIC1 GHR TGFBI 0.936

 13

 BDNF PA2G4 MMP7 GPI GHR CLICK1 C9 STX1A 0.936

 14

 KLK3-SERPINA3 STX1A BDNF PA2G4 MMP7 CLICK1 GHR TGFBI 0.936

 fifteen

 KLK3-SERPINA3 STX1A BDNF NME2 MMP7 CLICK1 TGFBI LRIG3 0.936

 16

 BDNF AHSG CDH1 STX1A MMP7 CLIC1 GHR TGFBI 0.936

 17

 KLK3-SERPINA3 STX1A BDNF PA2G4 MMP7 CLICK1 GHR AHSG 0.936

 18

 BDNF STX1A EGFR GPI MMP7 CLIC1 GHR TGFBI 0.936

 19

 KLK3-SERPINA3 STX1A BDNF NME2 CLICK KIT1 MMP7 GHR 0.936

 twenty

 KLK3-SERPINA3 PA2G4 BDNF GPI MMP7 CLICK1 GHR STX1A 0.936

 twenty-one

 BDNF STX1A GHR CRP TGFBI CLICK1 LRIG3 AHSG 0.936

 22

 BDNF TPI1 MMP7 CRP GHR CLICK1 TGFBI STX1A 0.936

 2. 3

 BDNF STX1A KIT NME2 MMP7 CLICK1 GHR LRIG3 0.936

 24

 BDNF STX1A KIT PA2G4 MMP7 CLICK1 GHR C9 0.936

 25

 KLK3-SERPINA3 STX1A BDNF TPT1 CDH1 CLICK1 MMP7 LRIG3 0.936

 26

 KLK3-SERPINA3 TPI1 BDNF ITIH4 CLICK KIT1 MMP7 STX1A 0.936

 27

 KLK3-SERPINA3 STX1A BDNF GPI MMP7 CLICK1 GHR TGFBI 0.935

 28

 BDNF STX1A KIT TPT1 MMP7 CLIC1 GHR C9 0.935

 29

 KLK3-SERPINA3 STX1A BDNF GPI MMP7 CLICK1 TGFBI LRIG3 0.935

 30

 KLK3-SERPINA3 NME2 BDNF ITIH4 KIT CLICK1 MMP7 STX1A 0.935

 31

 BDNF STX1A MMP7 GPI GHR CLICK1 TGFBI AHSG 0.935

 32

 BDNF PA2G4 ITIH4 MMP7 KIT CLIC1 GHR STX1A 0.935

 33

 KLK3-SERPINA3 STX1A BDNF NME2 MMP7 CLICK1 LRIG3 CHRDL1 0.935

 3. 4

 BDNF PA2G4 MMP7 CRP GHR CLICK1 TGFBI STX1A 0.935

 35

 BDNF STX1A MMP7 CRP GHR CLICK1 TGFBI LRIG3 0.935

 36

 BDNF GPI MMP7 CRP GHR CLICK1 TGFBI LRIG3 0.935

 37

 KLK3-SERPINA3 STX1A BDNF TPI1 MMP7 CLICK1 LRIG3 CHRDL1 0.935

 38

 KLK3-SERPINA3 LRIG3 BDNF STX1A KIT CLICK1 CDH1 MMP7 0.935

 39

 KLK3-SERPINA3 STX1A BDNF TPT1 MMP7 CLICK1 LRIG3 CHRDL1 0.935

 40

 BDNF STX1A GHR CRP TGFBI CLICK1 CHRDL1 AHSG 0.935

 41

 KLK3-SERPINA3 STX1A BDNF TPT1 CLICK KIT1 MMP7 LRIG3 0.935

 42

 KLK3-SERPINA3 STX1A BDNF TPI1 MMP7 CLICK1 GHR TGFBI 0.935

 43

 KLK3-SERPINA3 STX1A BDNF PA2G4 MMP7 CLICK1 GHR CHRDL1 0.935

 44

 BDNF STX1A MMP7 PA2G4 GHR CLICK1 TGFBI CHRDL1 0.935

 Four. Five

 KLK3-SERPINA3 STX1A BDNF TPT1 MMP7 CLICK1 GHR CHRDL1 0.935

 46

 BDNF STX1A MMP7 TPT1 GHR CLICK1 C9 TGFBI 0.935

 47

 KLK3-SERPINA3 TGFBI BDNF STX1A KIT CLICK1 MMP7 GHR 0.935

 48

 KLK3-SERPINA3 STX1A BDNF TPI1 CLICK KIT1 MMP7 LRIG3 0.935

 49

 BDNF CRP MMP7 ITIH4 GHR CLICK1 STX1A TPI1 0.935

 fifty

 KLK3-SERPINA3 PA2G4 BDNF ITIH4 KIT CLIC1 MMP7 STX1A 0.935

 51

 BDNF STX1A CDH1 CRP MMP7 CLIC1 GHR TGFBI 0.935

 52

 BDNF STX1A MMP7 NME2 GHR CLICK1 C9 TGFBI 0.935

 53

 BDNF KIT MMP7 GHR C9 0.935

 STX1A GPI CLICK1

 54

 KLK3-SERPINA3 BDNF MMP7 GHR LRIG3 0.935

 STX1A TPT1 CLICK1

 55

 BDNF KIT MMP7 GHR TGFBI 0.935

 STX1A PA2G4 CLICK1

 56

 BDNF MMP7 GHR C9 AHSG 0.935

 STX1A NME2 CLICK1

 57

 KLK3-SERPINA3 BDNF MMP7 GHR LRIG3 0.935

 STX1A GPI CLICK1

 58

 BDNF MMP7 GHR STX1A NME2 0.935

 GPI CRP CLICK1

 59

 BDNF GHR TGFBI LRIG3 CHRDL1 0.935

 AHSG CRP CLICK1

 60

 KLK3-SERPINA3 BDNF MMP7 GHR STX1A 0.935

 NME2 CLIC1 PLA2G7

 61

 BDNF KIT MMP7 GHR STX1A 0.935

 TPI1 ITIH4 CLICK1

 62

 BDNF MMP7 GHR C9 STX1A 0.935

 NME2 CLIC1 PLA2G7

 63

 BDNF MMP7 GHR C9 STX1A 0.935

 NME2 ITIH4 CLICK1

 64

 BDNF MMP7 GHR LRIG3 STX1A 0.935

 NME2 CRP CLICK1

 65

 BDNF MMP7 GHR C9 TGFBI 0.935

 STX1A YWHAG CLICK1

 66

 BDNF MMP7 GHR CHRDL1 STX1A 0.935

 PA2G4 CRP CLICK1

 67

 BDNF MMP7 GHR TGFBI LRIG3 0.935

 STX1A PA2G4 CLICK1

 68

 BDNF MMP7 GHR C9 STX1A 0.935

 TPI1 ITIH4 CLICK1

 69

 BDNF EGFR MMP7 GHR TGFBI 0.935

 AHSG STX1A CLICK1

 70

 BDNF MMP7 GHR CHRDL1 STX1A 0.935

 TPT1 CRP CLICK1

 71

 BDNF MMP7 GHR STX1A NME2 0.935

 CRP ITIH4 CLICK1

 72

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.935

 LRIG3 STX1A CLICK1

 73

 KLK3-SERPINA3 BDNF MMP7 TGFBI LRIG3 0.935

 STX1A TPI1 CLICK1

 74

 BDNF CRP MMP7 CLIC1 GHR PLA2G7 TGFBI STX1A 0.935

 75

 KLK3-SERPINA3 STX1A BDNF PA2G4 MMP7 CLICK1 TGFBI CHRDL1 0.935

 76

 BDNF PA2G4 MMP7 GPI GHR CLICK1 TGFBI STX1A 0.934

 77

 BDNF STX1A MMP7 GPI GHR CLICK1 C9 TGFBI 0.934

 78

 BDNF STX1A GHR CRP TGFBI CLIC1 LRIG3 CHRDL1 0.934

 79

 BDNF TPI1 MMP7 ITIH4 GHR CLICK1 TGFBI STX1A 0.934

 80

 KLK3-SERPINA3 STX1A BDNF CRP TGFBI CLICK1 LRIG3 CHRDL1 0.934

 81

 BDNF STX1A MMP7 TPT1 GHR CLICK1 C9 CHRDL1 0.934

 82

 KLK3-SERPINA3 STX1A BDNF HNRNPAB CLICK KIT MMP7 GHR 0.934

 83

 BDNF NME2 MMP7 CRP GHR CLICK1 CHRDL1 STX1A 0.934

 84

 KLK3-SERPINA3 STX1A BDNF NME2 EGFR CLIC1 MMP7 TGFBI 0.934

 85

 BDNF STX1A KIT HNRNPAB MMP7 CLICK1 GHR C9 0.934

 86

 BDNF NME2 MMP7 GPI GHR CLICK1 C9 STX1A 0.934

 87

 BDNF LRIG3 KIT STX1A MMP7 CLICK1 GHR TGFBI 0.934

 88

 BDNF STX1A MMP7 CLIC1 GHR PLA2G7 TGFBI AHSG 0.934

 89

 KLK3-SERPINA3 STX1A BDNF HNRNPAB CLICK KIT MMP7 LRIG3 0.934

 90

 BDNF STX1A KIT NME2 MMP7 CLICK1 GHR TGFBI 0.934

 91

 BDNF CRP MMP7 CLIC1 GHR PLA2G7 STX1A NME2 0.934

 92

 KLK3-SERPINA3 STX1A BDNF NME2 MMP7 CLICK1 GHR AHSG 0.934

 93

 KLK3-SERPINA3 STX1A BDNF TPI1 MMP7 CLICK1 GHR AHSG 0.934

 94

 BDNF CRP MMP7 HNRNPAB GHR CLICK1 TGFBI STX1A 0.934

 95

 BDNF PA2G4 MMP7 GPI GHR CLICK1 CHRDL1 STX1A 0.934

 96

 KLK3-SERPINA3 STX1A BDNF PA2G4 CLIC1 KIT MMP7 LRIG3 0.934

 97

 BDNF STX1A MMP7 PA2G4 GHR CLICK1 CHRDL1 AHSG 0.934

 98

 BDNF GPI MMP7 CLIC1 GHR PLA2G7 TGFBI STX1A 0.934

 99

 KLK3-SERPINA3 STX1A BDNF TPT1 MMP7 CLICK1 GHR C9 0.934

 100

 BDNF TPT1 MMP7 ITIH4 GHR CLICK1 C9 STX1A 0.934

Table 10: Panels of 9 biomarkers

 Markers

 VC ABC

 one

 BDNF MMP7 GHR TGFBI LRIG3 0.941

 STX1A NME2 CRP CLICK1

 2

 BDNF MMP7 GHR TGFBI CHRDL1 0.941

 STX1A PA2G4 CRP CLICK1

 3

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.941

 TGFBI STX1A TPI1 CLICK1

 4

 BDNF KIT MMP7 GHR LRIG3 0.941

 STX1A NME2 CRP CLICK1

 5

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.941

 TGFBI STX1A PA2G4 CLICK1

 6

 BDNF MMP7 GHR TGFBI CHRDL1 0.941

 STX1A TPI1 CRP CLICK1

 7

 KLK3-SERPINA3 BDNF MMP7 TGFBI LRIG3 0.940

 CHRDL1 STX1A NME2 CLICK1

 8

 BDNF MMP7 GHR TGFBI LRIG3 0.940

 STX1A GPI CRP CLIC1

 9

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.940

 CHRDL1 STX1A TPI1 CLICK1

 10

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.940

 LRIG3 STX1A NME2 CLICK1

 eleven

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.940

 LRIG3 STX1A TPI1 CLICK1

 12

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.940

 LRIG3 STX1A GPI CLICK1

 13

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.940

 STX1A PA2G4 GPI CLICK1

 14

 BDNF EGFR MMP7 GHR TGFBI 0.940

 AHSG STX1A NME2 CLICK1

 fifteen

 BDNF EGFR MMP7 GHR TGFBI 0.940

 STX1A NME2 CRP CLICK1

 16

 BDNF STX1A MMP7 NME2 GHR CRP TGFBI CLIC1 CHRDL1 0.940

 17

 BDNF KIT MMP7 GHR C9 0.940

 STX1A PA2G4 GPI CLICK1

 18

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.940

 LRIG3 STX1A PA2G4 CLICK1

 19

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.940

 LRIG3 STX1A NME2 CLICK1

 twenty

 BDNF MMP7 GHR TGFBI AHSG 0.940

 STX1A GPI CRP CLIC1

 twenty-one

 BDNF MMP7 GHR TGFBI CHRDL1 0.940

 STX1A TPT1 CRP CLIC1

 22

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.940

 LRIG3 STX1A TPT1 CLICK1

 2. 3

 BDNF CDH1 MMP7 GHR TGFBI 0.940

 STX1A NME2 CRP CLICK1

 24

 BDNF MMP7 GHR TGFBI CHRDL1 0.940

 AHSG STX1A PA2G4 CLICK1

 25

 BDNF MMP7 GHR TGFBI STX1A 0.940

 NME2 GPI CRP CLICK1

 26

 BDNF KIT MMP7 GHR STX1A 0.940

 TPI1 CRP ITIH4 CLICK1

 27

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.939

 CHRDL1 STX1A TPT1 CLICK1

 28

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.939

 TGFBI LRIG3 STX1A CLICK1

 29

 BDNF IGFBP2 MMP7 TGFBI LRIG3 0.939

 STX1A NME2 CRP CLICK1

 30

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.939

 AHSG STX1A PA2G4 CLICK1

 31

 BDNF MMP7 GHR C9 CHRDL1 0.939

 STX1A PA2G4 GPI CLICK1

 32

 BDNF MMP7 GHR CHRDL1 STX1A 0.939

 TPI1 CRP ITIH4 CLICK1

 33

 KLK3-SERPINA3 BDNF MMP7 TGFBI LRIG3 0.939

 CHRDL1 STX1A TPI1 CLICK1

 3. 4

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.939

 TGFBI STX1A NME2 CLICK1

 35

 KLK3-SERPINA3 BDNF MMP7 GHR CHRDL1 0.939

 AHSG STX1A TPI1 CLICK1

 36

 BDNF MMP7 GHR TGFBI STX1A 0.939

 PA2G4 CRP ITIH4 CLICK1

 37

 BDNF KIT MMP7 GHR C9 0.939

 STX1A TPI1 ITIH4 CLICK1

 38

 KLK3-SERPINA3 STX1A BDNF NME2 MMP7 PA2G4 GHR CLICK1 CHRDL1 0.939

 39

 BDNF PA2G4 CRP MMP7 KIT ITIH4 GHR CLICK1 STX1A 0.939

 40

 BDNF PA2G4 MMP7 GPI GHR CRP CHRDL1 CLICK1 STX1A 0.939

 41

 KLK3-SERPINA3 LRIG3 BDNF STX1A IGFBP2 NME2 MMP7 CLICK1 TGFBI 0.939

 42

 BDNF NME2 MMP7 CRP GHR ITIH4 TGFBI CLIC1 STX1A 0.939

 43

 KLK3-SERPINA3 LRIG3 BDNF STX1A KIT HNRNPAB MMP7 CLICK1 GHR 0.939

 44

 BDNF LRIG3 KIT STX1A MMP7 NME2 GHR CLICK1 TGFBI 0.939

 Four. Five

 BDNF STX1A MMP7 NME2 GHR GPI C9 CLICK1 TGFBI 0.939

 46

 KLK3-SERPINA3 STX1A BDNF NME2 MMP7 CRP GHR CLIC1 TGFBI 0.939

 47

 KLK3-SERPINA3 STX1A BDNF GPI MMP7 CRP GHR CLIC1 TGFBI 0.939

 48

 KLK3-SERPINA3 STX1A BDNF TPI1 MMP7 CRP GHR CLIC1 TGFBI 0.939

 49

 BDNF STX1A KIT TPI1 MMP7 CRP GHR CLICK1 TGFBI 0.939

 fifty

 BDNF SERPINA1 MMP7 STX1A GHR TPI1 TGFBI CLICK1 CHRDL1 0.939

 51

 KLK3-SERPINA3 LRIG3 BDNF STX1A KIT TPI1 MMP7 CLICK1 TGFBI 0.939

 52

 BDNF STX1A KIT NME2 MMP7 CRP GHR CLICK1 TGFBI 0.939

 53

 BDNF LRIG3 KIT STX1A MMP7 CRP GHR CLICK1 TGFBI 0.939

 54

 KLK3-SERPINA3 STX1A BDNF PA2G4 KIT ITIH4 MMP7 CLICK1 GHR 0.939

 55

 KLK3-SERPINA3 STX1A BDNF PA2G4 MMP7 GPI GHR CLICK1 CHRDL1 0.939

 56

 BDNF AHSG GHR STX1A TGFBI CRP LRIG3 CLICK1 CHRDL1 0.939

 57

 KLK3-SERPINA3 GHR BDNF STX1A KIT TPT1 CDH1 CLICK1 MMP7 0.939

 58

 KLK3-SERPINA3 TGFBI BDNF STX1A EGFR PA2G4 MMP7 CLIC1 GHR 0.939

 59

 KLK3-SERPINA3 AHSG BDNF STX1A MMP7 GPI GHR CLIC1 TGFBI 0.939

 60

 KLK3-SERPINA3 LRIG3 BDNF STX1A KIT NME2 MMP7 CLICK1 TGFBI 0.939

 61

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.939

 CHRDL1 STX1A PA2G4 CLICK1

 62

 BDNF KIT MMP7 GHR TGFBI 0.939

 LRIG3 STX1A TPI1 CLICK1

 63

 BDNF CDH1 MMP7 GHR TGFBI 0.939

 AHSG STX1A CRP CLICK1

 64

 BDNF MMP7 GHR CHRDL1 AHSG 0.939

 STX1A PA2G4 CRP CLICK1

 65

 BDNF KIT MMP7 GHR TGFBI 0.939

 STX1A TPI1 ITIH4 CLICK1

 66

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.939

 AHSG STX1A PA2G4 CLICK1

 67

 BDNF EGFR MMP7 GHR TGFBI 0.939

 AHSG STX1A CLIC1 PLA2G7

 68

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.938

 TGFBI STX1A TPT1 CLICK1

 69

 KLK3-SERPINA3 BDNF MMP7 CHRDL1 STX1A 0.938

 NME2 PA2G4 ITIH4 CLICK1

 70

 KLK3-SERPINA3 BDNF KIT MMP7 LRIG3 0.938

 STX1A TPI1 ITIH4 CLICK1

 71

 BDNF KIT MMP7 GHR C9 0.938

 LRIG3 STX1A NME2 CLICK1

 72

 BDNF KIT MMP7 GHR TGFBI 0.938

 LRIG3 STX1A GPI CLICK1

 73

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.938

 STX1A NME2 GPI CLICK1

 74

 KLK3-SERPINA3 BDNF KIT CDH1 MMP7 0.938

 GHR STX1A PA2G4 CLICK1

 75

 KLK3-SERPINA3 BDNF EGFR MMP7 TGFBI 0.938

 LRIG3 STX1A NME2 CLICK1

 76

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.938

 LRIG3 STX1A GPI CLICK1

 77

 BDNF KIT MMP7 GHR LRIG3 0.938

 STX1A TPI1 CRP CLICK1

 78

 KLK3-SERPINA3 BDNF MMP7 GHR AHSG 0.938

 STX1A PA2G4 GPI CLICK1

 79

 BDNF KIT MMP7 GHR TGFBI 0.938

 STX1A PA2G4 ITIH4 CLICK1

 80

 KLK3-SERPINA3 BDNF MMP7 GHR CHRDL1 0.938

 STX1A TPT1 PA2G4 CLICK1

 81

 BDNF MMP7 GHR CHRDL1 AHSG 0.938

 STX1A TPI1 CRP CLICK1

 82

 KLK3-SERPINA3 LRIG3 BDNF STX1A KIT NME2 CDH1 CLICK1 MMP7 0.938

 83

 BDNF KIT MMP7 GHR C9 0.938

 STX1A PA2G4 ITIH4 CLICK1

 84

 BDNF IGFBP2 MMP7 GHR TGFBI 0.938

 AHSG STX1A TPI1 CLICK1

 85

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.938

 STX1A TPI1 ITIH4 CLICK1

 86

 BDNF MMP7 GHR CHRDL1 AHSG 0.938

 STX1A TPT1 CRP CLIC1

 87

 BDNF MMP7 GHR TGFBI STX1A 0.938

 TPI1 CRP ITIH4 CLICK1

 88

 BDNF KIT MMP7 GHR STX1A 0.938

 NME2 CRP ITIH4 CLICK1

 89

 KLK3-SERPINA3 BDNF EGFR MMP7 GHR 0.938

 AHSG STX1A PA2G4 CLICK1

 90

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.938

 CHRDL1 STX1A NME2 CLICK1

 91

 BDNF MMP7 GHR TGFBI LRIG3 0.938

 STX1A NME2 GPI CLICK1

 92

 BDNF CDH1 MMP7 GHR TGFBI 0.938

 LRIG3 AHSG STX1A CLICK1

 93

 BDNF CDH1 MMP7 GHR TGFBI 0.938

 STX1A GPI CRP CLIC1

 94

 KLK3-SERPINA3 BDNF MMP7 GHR STX1A 0.938

 NME2 GPI CRP CLICK1

 95

 BDNF MMP7 GHR C9 CHRDL1 0.938

 AHSG STX1A TPI1 CLICK1

 96

 BDNF KIT MMP7 GHR TGFBI 0.938

 STX1A PA2G4 CRP CLICK1

 97

 KLK3-SERPINA3 BDNF EGFR MMP7 GHR 0.938

 TGFBI STX1A TPI1 CLICK1

 98

 BDNF MMP7 GHR TGFBI STX1A 0.938

 NME2 CRP CLIC1 PLA2G7

 99

 BDNF MMP7 GHR TGFBI BMP1 0.938

 STX1A NME2 CRP CLICK1

 100

 BDNF EGFR MMP7 GHR TGFBI 0.938

 STX1A GPI CRP CLIC1

Table 11: 10 biomarker panels

 Markers

 VC ABC

 one

 BDNF MMP7 GHR TGFBI CHRDL1 0.944

 SERPINA1 STX1A NME2 PA2G4 CLICK1

 2

 BDNF MMP7 GHR TGFBI CHRDL1 0.944

 AHSG STX1A TPI1 CRP CLICK1

 3

 BDNF LRIG3 KIT STX1A MMP7 NME2 GHR CRP TGFBI CLIC1 0.944

 4

 BDNF MMP7 GHR TGFBI CHRDL1 0.944

 STX1A PA2G4 CRP ITIH4 CLICK1

 5

 BDNF KIT MMP7 GHR TGFBI 0.944

 STX1A TPI1 CRP ITIH4 CLICK1

 6

 BDNF MMP7 GHR TGFBI CHRDL1 0.943

 STX1A TPI1 CRP ITIH4 CLICK1

 7

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.943

 CHRDL1 AHSG STX1A TPI1 CLICK1

 8

 BDNF MMP7 GHR TGFBI LRIG3 0.943

 CHRDL1 STX1A TPI1 CRP CLICK1

 9

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.943

 CHRDL1 STX1A TPI1 CRP CLICK1

 10

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.943

 TGFBI LRIG3 STX1A TPI1 CLICK1

 eleven

 BDNF MMP7 GHR TGFBI CHRDL1 0.943

 STX1A PA2G4 GPI CRP CLICK1

 12

 BDNF MMP7 GHR TGFBI CHRDL1 0.943

 AHSG STX1A NME2 CRP CLICK1

 13

 BDNF IGFBP2 MMP7 GHR TGFBI 0.943

 LRIG3 STX1A NME2 CRP CLICK1

 14

 BDNF KIT MMP7 GHR TGFBI 0.943

 STX1A PA2G4 CRP ITIH4 CLICK1

 fifteen

 BDNF KIT MMP7 GHR TGFBI 0.943

 LRIG3 STX1A TPI1 CRP CLICK1

 16

 BDNF MMP7 GHR C9 TGFBI 0.943

 CHRDL1 STX1A PA2G4 GPI CLICK1

 17

 BDNF MMP7 GHR TGFBI CHRDL1 0.943

 AHSG STX1A PA2G4 CRP CLICK1

 18

 BDNF MMP7 GHR TGFBI CHRDL1 0.943

 STX1A NME2 PA2G4 CRP CLICK1

 19

 BDNF EGFR MMP7 GHR TGFBI 0.943

 CHRDL1 STX1A TPI1 CRP CLICK1

 twenty

 BDNF MMP7 GHR TGFBI CHRDL1 0.943

 STX1A NME2 CRP ITIH4 CLICK1

 twenty-one

 BDNF MMP7 GHR CHRDL1 STX1A 0.943

 NME2 PA2G4 CRP ITIH4 CLICK1

 22

 BDNF MMP7 GHR TGFBI LRIG3 0.942

 CHRDL1 STX1A NME2 CRP CLICK1

 2. 3

 KLK3-SERPINA3 BDNF MMP7 GHR CHRDL1 0.942

 STX1A NME2 PA2G4 CRP CLICK1

 24

 KLK3-SERPINA3 BDNF MMP7 TGFBI LRIG3 0.942

 CHRDL1 SERPINA1 STX1A TPI1 CLICK1

 25

 KLK3-SERPINA3 AHSG BDNF STX1A 32 | "0 -vi GHR CRP CHRDL1 CLICK1 0.942

 26

 BDNF LRIG3 KIT STX1A MMP7 TPT1 GHR CRP TGFBI CLIC1 0.942

 27

 BDNF AHSG MMP7 STX1A GHR NME2 TGFBI CRP LRIG3 CLICK1 0.942

 28

 KLK3-SERPINA3 AHSG BDNF STX1A MMP7 PA2G4 GHR GPI CHRDL1 CLICK1 0.942

 29

 BDNF AHSG MMP7 STX1A GHR TPT1 C9 PA2G4 CHRDL1 CLICK1 0.942

 30

 KLK3-SERPINA3 TGFBI BDNF AHSG EGFR STX1A MMP7 TPI1 GHR CLICK1 0.942

 31

 KLK3-SERPINA3 TGFBI BDNF AHSG EGFR STX1A MMP7 NME2 GHR CLICK1 0.942

 32

 BDNF AHSG EGFR STX1A MMP7 NME2 GHR ITIH4 TGFBI CLICK1 0.942

 33

 BDNF STX1A MMP7 NME2 GHR GPI TGFBI CRP LRIG3 CLICK1 0.942

 3. 4

 BDNF CHRDL1 MMP7 STX1A GHR NME2 C9 PA2G4 TGFBI CLICK1 0.942

 35

 KLK3-SERPINA3 CHRDL1 BDNF STX1A MMP7 PA2G4 GHR CRP TGFBI CLICK1 0.942

 36

 BDNF AHSG MMP7 STX1A GHR PA2G4 TGFBI ITIH4 CHRDL1 CLICK1 0.942

 37

 KLK3-SERPINA3 LRIG3 BDNF AHSG KIT STX1A MMP7 NME2 GHR CLICK1 0.942

 38

 BDNF TGFBI KIT STX1A EGFR TPI1 MMP7 ITIH4 GHR CLICK1 0.942

 39

 KLK3-SERPINA3 CHRDL1 BDNF AHSG MMP7 STX1A GHR PA2G4 TGFBI CLICK1 0.942

 40

 KLK3-SERPINA3 LRIG3 BDNF STX1A MMP7 GPI GHR CRP TGFBI CLIC1 0.942

 41

 BDNF STX1A MMP7 PA2G4 GHR GPI CHRDL1 CRP AHSG CLICK1 0.942

 42

 BDNF LRIG3 KIT STX1A MMP7 TPI1 GHR ITIH4 TGFBI CLIC1 0.942

 43

 KLK3-SERPINA3 TGFBI BDNF AHSG KIT STX1A MMP7 PA2G4 GHR CLICK1 0.942

 44

 KLK3-SERPINA3 TGFBI BDNF LRIG3 KIT STX1A MMP7 PA2G4 GHR CLICK1 0.942

 Four. Five

 KLK3-SERPINA3 CHRDL1 BDNF STX1A MMP7 NME2 GHR PA2G4 TGFBI CLICK1 0.942

 46

 KLK3-SERPINA3 TGFBI BDNF LRIG3 KIT STX1A MMP7 NME2 GHR CLICK1 0.942

 47

 BDNF MMP7 GHR C9 CHRDL1 0.942

 AHSG STX1A PA2G4 GPI CLICK1

 48

 BDNF MMP7 GHR C9 CHRDL1 0.942

 AHSG GPI TPI1 CRP CLICK1

 49

 BDNF CDH1 MMP7 GHR TGFBI 0.942

 LRIG3 STX1A NME2 CRP CLICK1

 fifty

 BDNF EGFR MMP7 GHR TGFBI 0.942

 AHSG STX1A NME2 CRP CLICK1

 51

 BDNF MMP7 GHR TGFBI CHRDL1 0.942

 SERPINA1 STX1A TPI1 CRP CLICK1

 52

 BDNF CDH1 MMP7 GHR CHRDL1 0.942

 AHSG STX1A TPT1 CRP CLICK1

 53

 BDNF EGFR MMP7 GHR TGFBI 0.942

 STX1A TPI1 CRP ITIH4 CLICK1

 54

 KLK3-SERPINA3 BDNF MMP7 GHR TGFBI 0.942

 LRIG3 CHRDL1 STX1A TPI1 CLICK1

 55

 BDNF MMP7 GHR TGFBI LRIG3 0.942

 CHRDL1 STX1A CRP HNRNPAB CLICK1

 56

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.942

 TGFBI LRIG3 STX1A TPT1 CLICK1

 57

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.942

 LRIG3 STX1A TPI1 ITIH4 CLICK1

 58

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.942

 TGFBI STX1A PA2G4 CRP CLICK1

 59

 BDNF MMP7 GHR TGFBI CHRDL1 0.942

 STX1A NME2 CRP CLIC1 PLA2G7

 60

 BDNF EGFR MMP7 GHR C9 0.942

 TGFBI AHSG STX1A NME2 CLICK1

 61

 BDNF KIT MMP7 GHR TGFBI 0.942

 CHRDL1 TPI1 CRP ITIH4 CLICK1

 62

 BDNF MMP7 GHR TGFBI LRIG3 0.942

 CHRDL1 STX1A GPI CRP CLICK1

 63

 BDNF MMP7 GHR TGFBI LRIG3 0.942

 CHRDL1 AHSG STX1A NME2 CLICK1

 64

 KLK3-SERPINA3 BDNF KIT MMP7 GHR 0.942

 TGFBI STX1A PA2G4 ITIH4 CLICK1

 65

 KLK3-SERPINA3 BDNF KIT EGFR MMP7 0.942

 GHR TGFBI STX1A PA2G4 CLICK1

 66

 KLK3-SERPINA3 BDNF KIT CDH1 MMP7 0.942

 GHR LRIG3 STX1A NME2 CLICK1

 67

 BDNF MMP7 GHR TGFBI CHRDL1 0.942

 STX1A PA2G4 GPI ITIH4 CLICK1

 68

 BDNF MMP7 GHR TGFBI LRIG3 0.942

 CHRDL1 GPI TPI1 CRP CLICK1

 69

 KLK3-SERPINA3 STX1A BDNF GPI 32 | "0 -vi GHR CRP TGFBI CLICK1 0.942

 70

 BDNF FN1 MMP7 STX1A GHR TPI1 TGFBI CRP CHRDL1 CLICK1 0.942

 71

 BDNF STX1A EGFR NME2 MMP7 CRP GHR TIH4 TGFBI CLIC1 0.942

 72

 BDNF SERPINA1 MMP7 AHSG GHR STX1A TGFBI TPI1 CHRDL1 CLICK1 0.942

 73

 BDNF AHSG CDH1 STX1A MMP7 PA2G4 GHR CRP CHRDL1 CLICK1 0.942

 74

 BDNF STX1A MMP7 GPI GHR TPI1 TGFBI CRP CHRDL1 CLICK1 0.942

 75

 BDNF CHRDL1 HMGB1 AHSG MMP7 STX1A GHR CRP TGFBI CLICK1 0.942

 76

 BDNF STX1A MMP7 NME2 GHR GPI TGFBI CRP CHRDL1 CLICK1 0.942

 77

 KLK3-SERPINA3 TGFBI BDNF LRIG3 KIT CHRDL1 MMP7 TPI1 GHR CLICK1 0.942

 78

 BDNF LRIG3 KIT CHRDL1 MMP7 NME2 GHR CRP TGFBI CLICK1 0.942

 79

 BDNF CHRDL1 MMP7 AHSG GHR TPI1 C9 CRP TGFBI CLIC1 0.942

 80

 BDNF AHSG MMP7 STX1A GHR GPI C9 TPI1 CHRDL1 CLICK1 0.942

 81

 BDNF CHRDL1 EGFR AHSG MMP7 STX1A GHR NME2 TGFBI CLICK1 0.942

 82

 BDNF STX1A MMP7 NME2 GHR CRP TGFBI CLICK1 LRIG3 PLA2G7 0.941

 83

 BDNF CHRDL1 MMP7 AHSG GHR STX1A C9 TPT1 TGFBI CLICK1 0.941

 84

 BDNF SERPINA1 MMP7 STX1A GHR PA2G4 TGFBI GPI CHRDL1 CLICK1 0.941

 85

 BDNF AHSG MMP7 STX1A GHR PA2G4 TGFBI GPI CHRDL1 CLICK1 0.941

 86

 KLK3-SERPINA3 LRIG3 BDNF STX1A KIT NME2 MMP7 GPI GHR CLICK1 0.941

 87

 KLK3-SERPINA3 CHRDL1 BDNF AHSG MMP7 STX1A GHR TPI1 LRIG3 CLICK1 0.941

 88

 KLK3-SERPINA3 TGFBI BDNF CHRDL1 MMP7 STX1A GHR PA2G4 CNTN1 CLICK1 0.941

 89

 BDNF STX1A MMP7 NME2 GHR PA2G4 C9 ITIH4 CHRDL1 CLICK1 0.941

 90

 BDNF AHSG IGFBP2 STX1A MMP7 TPI1 GHR CRP TGFBI CLICK1 0.941

 91

 BDNF CHRDL1 MMP7 GPI GHR TPI1 C9 CRP TGFBI CLICK1 0.941

 92

 BDNF CHRDL1 MMP7 STX1A GHR TPT1 TGFBI CRP LRIG3 CLICK1 0.941

 93

 KLK3-SERPINA3 LRIG3 BDNF STX1A MMP7 NME2 GHR CRP TGFBI CLICK1 0.941

 94

 BDNF CHRDL1 EGFR STX1A MMP7 NME2 GHR CRP TGFBI CLICK1 0.941

 95

 BDNF LRIG3 KIT STX1A MMP7 NME2 GHR CRP C9 CLICK1 0.941

 96

 BDNF LRIG3 CDH1 AHSG MMP7 STX1A GHR CRP TGFBI CLICK1 0.941

 97

 BDNF STX1A MMP7 GPI GHR TPI1 CHRDL1 CRP AHSG CLICK1 0.941

 98

 BDNF STX1A KIT NME2 MMP7 GPI GHR CRP LRIG3 CLICK1 0.941

 99

 KLK3-SERPINA3 AHSG BDNF STX1A MMP7 TPI1 GHR CRP TGFBI CLICK1 0.941

 100

 BDNF AHSG MMP7 STX1A GHR NME2 C9 GPI CHRDL1 CLICK1 0.941

Table 12: Marker counts on biomarker panels

 Panel size

 Biomarker

 3 4 5 6 7 8 9 10

 AHSG

 37 45 59 85 116 159 222 349

 AKR7A2

 87 48 23 9 3 3 1 0

 AKT3

 0 0 0 0 0 0 0 1

 BDNF

 53 129 332 583 801 953 988 995

 BMP1

 81 93 84 74 42 32 26 23

 BMPER

 13 1 0 0 0 0 0 0

 C9

 131 178 252 244 233 211 203 194

 CA6

 29 14 1 0 0 0 0 0

 CAPG

 6 0 0 0 0 0 0 0

 CDH1

 22 56 104 105 112 129 145 166

 CHRDL1

 50 61 81 98 116 170 304 477

 CKB-CKM

 26 18 8 8 6 2 0 1

 CLICK1

 260 447 669 883 978 994 1000 1000

 CMA1

 84 119 189 158 99 62 37 19

 CNTN1

 20 52 61 59 42 30 31 29

 COL18A1

 25 17 7 0 1 0 0 0

 CRP

 74 89 95 112 153 200 308 454

 CTSL2

 2 0 0 0 0 0 0 0

 DDC

 37 23 7 5 4 0 0 0

 EGFR

 63 47 27 41 50 88 100 121

 FGA-FGB-FGG

 23 0 0 0 0 0 0 0

 FN1

 3 0 0 2 0 2 8 18

 GHR

 32 67 159 315 452 587 745 850

 GPI

 71 79 103 147 167 183 202 225

 HMGB1

 15 36 11 17 19 4 6 4

 HNRNPAB

 46 27 35 45 60 41 38 32

 HP

 21 7 0 0 0 0 0 0

 HSP90AA1

 2 0 0 0 0 0 0 0

 HSPA1A

 6 2 0 0 0 0 0 0

 IGFBP2

 42 51 74 105 142 129 91 67

 IGFBP4

 19 6 1 3 2 0 5 6

 ITIH4

 23 46 51 64 117 163 180 208

 KIT

 21 26 30 51 109 203 295 327

 KLK3-SERPINA3

 111 188 262 287 307 338 377 378

 L1CAM

 41 45 44 16 9 8 3 8

 LRIG3

 109 161 241 293 330 367 376 407

 MMP12

 71 29 5 2 0 0 0 0

 MMP7

 270 626 782 852 916 960 982 996

 NME2

 83 77 112 159 189 251 282 299

 PA2G4

 7 33 41 57 85 146 203 275

 PLA2G7

 17 32 28 30 47 67 70 66

 PLAUR

 33 22 11 5 0 0 0 0

 PRKACA

 8 0 0 0 0 0 0 0

 PRKCB

 3 0 0 0 0 0 0 0

 PROK11

 2 0 0 0 0 0 0 0

 PRSS2

 5 0 0 0 0 0 0 0

 PTN

 17 2 0 0 0 0 0 0

 SERPINA1

 51 35 23 16 29 36 43 68

 STC1

 17 10 7 4 4 8 8 7

 STX1A

 131 268 345 520 691 823 902 934

 TACSTD2

 7 1 2 0 1 0 0 3

 TGFBI

 62 98 136 191 266 339 462 579

 TPI1

 42 64 106 124 139 187 243 305

 TPT1

 54 33 22 29 67 88 108 108

 YWHAG

 419 492 369 202 96 37 6 1

 YWHAH

 16 0 1 0 0 0 0 0

 CLICK1

 BDNF

 MMP7

 STX1A

 GHR

 TGFBI

 CHRDL1

 CRP

 LRIG3

 KLK3-SERPINA3

 AHSG

 KIT

Table 14: Parameters obtained from the training set for simple Bayesian classifier.

 Biomarker

 V Vd Oc Od

 BMPER

 7,450 7,323 0.108 0.164

 COL18A1

 8,763 8,876 0.125 0.162

 CMA1

 6,800 6,754 0,047 0,041

 MMP7

 8,881 9,232 0.235 0.182

 KIT

 9,603 9,503 0,139 0,141

 IGFBP2

 8,514 9,006 0.417 0.448

 PROK11

 6,196 6,154 0.042 0.058

 DDC

 6,746 6,711 0,034 0,043

 PRKACA

 7,594 7,753 0,187 0,113

 FGA-FGB-FGG

 9,836 10,258 0.338 0.580

 CNTN1

 9,265 9,149 0,181 0,114

 CRP

 7,733 9,005 1,095 1,422

 HNRNPAB

 7,252 7,517 0.304 0.225

 HSP90AA1

 9,165 9,343 0.226 0.182

 PLA2G7

 10,131 9,952 0.277 0.184

 BDNF

 6,931 6,854 0.102 0.068

 AKR7A2

 6,761 7,155 0.432 0.248

 IGFBP4

 8,138 8,268 0,140 0,163

 PLAUR

 8,248 8,385 0,133 0,178

 C9

 11,715 11,936 0.189 0.223

 SERPINA1

 10,215 10,371 0.169 0.239

 STC1

 8,475 8,691 0,242 0,293

 HP

 11,848 12,057 0.222 0.196

 L1CAM

 7,893 7,721 0.226 0.152

 ITIH4

 10,596 10,738 0,121 0,227

 BMP1

 8,766 8,548 0.213 0.234

 TFF3

 8,288 8,536 0.195 0.307

 PRKCB

 6,817 6,780 0,051 0,060

 IL12B-IL23A

 6,189 6,153 0.037 0.039

 CLICK1

 7,907 8,260 0,259 0,230

 CDH1

 9,252 9,050 0,200 0,181

 CHRDL1

 8,665 8,938 0.215 0.388

 EGFR

 10,578 10,428 0,119 0,135

 ASGR1

 6,661 6,619 0,050 0,052

 TACSTD2

 6,879 6,849 0,040 0,043

 PRSS2

 10,080 10,457 0.421 0.529

 AKT3

 7,816 7,886 0,074 0,068

 HMGB1

 8,430 8,546 0,133 0,096

 CAPG

 7.271 7.602 0.272 0.277

 YWHAH

 7,644 7,774 0.107 0.105

 PTN

 8,149 8,250 0,116 0,152

 YWHAG

 8,156 8,496 0.205 0.187

 CTSL2

 6,262 6,207 0,063 0,069

 GHR

 7,724 7,595 0,135 0.102

 TGFBI

 9,944 9,777 0.178 0.239

 GPI

 7,506 7,760 0,278 0,260

 TPI1

 9,087 9,392 0.450 0.221

 STX1A

 7,186 7,143 0.035 0.033

 LRIG3

 7,411 7,301 0,090 0,092

 TPT1

 8,847 9,137 0.290 0.224

 PA2G4

 7,735 8,026 0.643 0.329

 NME2

 6,333 6,618 0,339 0,242

 CKB-CKM

 7,515 7,230 0.317 0.307

 CA6

 7,180 7,038 0.228 0.108

 AHSG

 11,197 11,107 0,149 0,134

 KLK3-SERPINA3

 8,102 8,327 0,194 0,330

 FN1

 9,286 9,058 0.239 0.325

 MMP12

 6,129 6,323 0,100 0,260

 HSPA1A

 8,819 9,011 0.316 0.224

Table 15: ABC for biomarker combinations as an example

 No.

                     ABC

 one

 MMP7 0.803

 2

 MMP7 CLICK1 0.883

 3

 MMP7 CLIC1 STX1A 0.901

 4

 MMP7 CLIC1 STX1A CHRDL1 0.899

 5

 MMP7 CLIC1 STX1A CHRDL1 PA2G4 0.912

 6

 MMP7 CLIC1 STX1A CHRDL1 PA2G4 SERPINA1 0.922

 7

 MMP7 CLIC1 STX1A CHRDL1 PA2G4 SERPINA1 BDNF 0.930

 8

 MMP7 CLIC1 STX1A CHRDL1 PA2G4 SERPINA1 BDNF GHR 0.937

 9

 MMP7 CLIC1 STX1A CHRDL1 PA2G4 SERPINA1 BDNF GHR TGFBI 0.944

 10

 MMP7 CLIC1 STX1A CHRDL1 PA2G4 SERPINA1 BDNF GHR TGFBI NME2 0.948

Table 16: Calculations obtained from the training set for simple Bayesian classifier.

 Biomarker

 V Vd Oc Od X P (c \ x) P (dlx) ln (p (d \ x) / p (c \ x))

 GHR

 7,724 7,595 0,135 0.102 7,860 1,778 0,136 -2,572

 SERPINA1

 10,215 10,371 0,169 0,239 10,573 0,252 1,166 1,531

 STX1A

 7,186 7,143 0,035 0,033 7,259 1,382 0,024 -4,053

 CHRDL1

 8,665 8,938 0.215 0.388 8.405 0.896 0.401 -0.804

 CLICK1

 7,907 8,260 0,259 0,230 8,068 1,267 1,226 -0,034

 PA2G4

 7,735 8,026 0.643 0.329 7.285 0.486 0.096 -1.622

 NME2

 6,333 6,618 0,339 0,242 6,322 1,175 0,783 -0,406

 MMP7

 8,881 9,232 0,235 0,182 8,684 1,194 0.023 -3,942

 TGFBI

 9,944 9,777 0,178 0,239 9,778 1,446 1,669 0.144

 BDNF

 6,931 6,854 0.102 0.068 6,904 3,768 4,484 0.174

5

Table 17: Clinical characteristics of the training set

 Metadata

 Levels Sick Control d value of p

 Samples

   218 46

 SEX

 F 118 36

 M 100 10 4.34e-03

 AGE

 Mean 57.2 67.3

 DT 10.2 10.8 2.35e-07

 STADIUM OF

 I 0 26

 CANCER

 II 0 4

 III 0 7

 IV 0 9 NaN

 USER OF

 Never 1 2

 TOBACCO

 Not indicated 3 10

 Past 84 24

 Current 130 10 1.27e-10

Table 18: Classifier proteins of ten biomarkers

 Biomarker

 UniProt ID Address * Biological process (GO)

 BDNF

 P23560 Descendent and response to tension cellular communication regulation of cell death signaling process

 MMP7

 P09237 Ascending proteolysis

 GHR

 P10912 Descendent and regulation of cell death signaling process signaling regulation of signaling path

 TGFBI

 Q15582 Descendent and cell proliferation regulation of cell adhesion

 CHRDL1

 Q9BU40 Ascending signaling

 SERPINA1

 P01009 Ascending voltage response

 STX1A

 Q16623 Descendent and cellular communication signaling

 NME2

 P22392 Ascending

 PA2G4

 Q9UQ80 Ascending cell proliferation

 CLICK1

 000299 Ascending signaling process

Table 19: General cancer biomarkers

 KLK3-SERPINA3

 EGFR

 BMPER

 FGA-FGB-FGG

 C9

 STX1A

 AKR7A2

 CKB-CKM

 DDC

 CA6

 IGFBP2

 IGFBP4

 FN1

 BMP1

 CRP

 KIT

 CNTN1

 SERPINA1

 BDNF

 GHR

 ITIH4

 NME2

 AHSG

Table 20: 1 biomarker panels

 Markers

 Average VC ABC

 one

 C9 0,792

 2

 KLK3-SERPINA3 0,782

 3

 CRP 0.763

 4

 BMPER 0.745

 5

 BMP1 0.732

 6

 KIT 0.729

 7

 AKR7A2 0.726

 8

 EGFR 0.726

 9

 ITIH4 0.721

 10

 IGFBP2 0.720

 eleven

 BDNF 0.720

 12

 STX1A 0.719

 13

 NME2 0.714

 14

 FGA-FGB-FGG 0.712

 fifteen

 CNTN1 0.708

 16

 CKB-CKM 0.708

 17

 AHSG 0.707

 18

 GHR 0.704

 19

 IGFBP4 0.703

 twenty

 CA6 0,700

 twenty-one

 DDC 0.696

 22

 FN1 0.694

 2. 3

 SERPINA1 0.688

Table 21: Panels of 2 biomarkers

 Markers

 Average VC ABC

 one

 C9 AKR7A2 0.832

 2

 KLK3-SERPINA3 AKR7A2 0.831

 3

 KLK3-SERPINA3 NME2 0.828

 4

 AKR7A2 CRP 0.827

 5

 KLK3-SERPINA3 EGFR 0.826

 6

 KLK3-SERPINA3 STX1A 0.826

 7

 C9 NME2 0.824

 8

 KLK3-SERPINA3 BDNF 0.823

 9

 KLK3-SERPINA3 IGFBP4 0.822

 10

 KLK3-SERPINA3 CA6 0.819

 eleven

 KIT C9 0.819

 12

 BDNF C9 0.818

 13

 KLK3-SERPINA3 BMP1 0.816

 14

 KLK3-SERPINA3 BMPER 0.816

 fifteen

 NME2 CRP 0.815

 16

 KLK3-SERPINA3 KIT 0.815

 17

 C9 BMPER 0.814

 18

 BMPER NME2 0.812

 19

 KLK3-SERPINA3 C9 0.811

 twenty

 KLK3-SERPINA3 CRP 0.811

 twenty-one

 C9 STX1A 0.811

 22

 EGFR C9 0.811

 2. 3

 BMPER AKR7A2 0.810

 24

 BMPER CRP 0.810

 25

 BDNF CRP 0.810

 26

 C9 DDC 0.809

 27

 KLK3-SERPINA3 CNTN1 0.809

 28

 KLK3-SERPINA3 IGFBP2 0.808

 29

 SERPINA1 AKR7A2 0.808

 30

 AKR7A2 ITIH4 0.808

 31

 C9 AHSG 0.807

 32

 IGFBP4 C9 0.807

 33

 KLK3-SERPINA3 DDC 0.807

 3. 4

 BMP1 AKR7A2 0.806

 35

 CNTN1 C9 0.806

 36

 STX1A CRP 0.805

 37

 IGFBP2 CRP 0.805

 38

 NME2 ITIH4 0.805

 39

 BMP1 CRP 0.805

 40

 KLK3-SERPINA3 AHSG 0.804

 41

 C9 CA6 0.803

 42

 C9 CRP 0.802

 43

 GHR C9 0.802

 44

 BDNF AKR7A2 0.802

 Four. Five

 KLK3-SERPINA3 FN1 0.801

 46

 BDNF KIT 0.801

 47

 KLK3-SERPINA3 GHR 0.799

 48

 EGFR ITIH4 0.799

 49

 C9 BMP1 0,798

 fifty

 CRP KIT 0,798

 51

 IGFBP2 C9 0,798

 52

 BMP1 NME2 0,797

 53

 C9 ITIH4 0,797

 54

 EGFR AKR7A2 0,797

 55

 NME2 FGA-FGB-FGG 0,796

 56

 EGFR CRP 0.795

 57

 IGFBP2 AKR7A2 0,795

 58

 STX1A ITIH4 0,795

 59

 SERPINA1 NME2 0,795

 60

 AKR7A2 KIT 0,795

 61

 IGFBP2 BMPER 0,794

 62

 CNTN1 AKR7A2 0,794

 63

 C9 FN1 0,794

 64

 AKR7A2 FGA-FGB-FGG 0,793

 65

 BDNF NME2 0,793

 66

 GHR CRP 0.792

 67

 AHSG AKR7A2 0,792

 68

 CNTN1 BMPER 0,791

 69

 BMP1 KIT 0,791

 70

 CNTN1 BMP1 0,791

 71

 0.790 BMPER KIT

 72

 KLK3-SERPINA3 ITIH4 0.790

 73

 DDC CRP 0,789

 74

 CA6 CRP 0.788

 75

 IGFBP4 AKR7A2 0,788

 76

 IGFBP4 CRP 0.788

 77

 GHR BMPER 0,787

 78

 IGFBP2 CNTN1 0,787

 79

 EGFR NME2 0,787

 80

 BMPER ITIH4 0.786

 81

 BDNF CNTN1 0,785

 82

 C9 CKB-CKM 0.785

 83

 GHR AKR7A2 0.785

 84

 FN1 CRP 0.784

 85

 BDNF BMPER 0.784

 86

 CNTN1 CRP 0.784

 87

 KLK3-SERPINA3 CKB-CKM 0,784

 88

 EGFR AHSG 0,783

 89

 EGFR BMPER 0,783

 90

 STX1A NME2 0,783

 91

 BMP1 BMPER 0,783

 92

 DDC ITIH4 0,783

 93

 CA6 BMPER 0,782

 94

 STX1A AKR7A2 0,781

 95

 CRP ITIH4 0,781

 96

 BDNF ITIH4 0.780

 97

 IGFBP2 ITIH4 0.780

 98

 AHSG NME2 0,779

 99

 CNTN1 NME2 0,779

 100

 CA6 AKR7A2 0,778

Table 22: 3 biomarker panels

 Markers

 Average VC ABC

 one

 IGFBP2 AKR7A2 CRP 0.849

 2

 KLK3-SERPINA3 BMPER NME2 0.849

 3

 KLK3-SERPINA3 C9 AKR7A2 0.848

 4

 KLK3-SERPINA3 AKR7A2 CRP 0.848

 5

 KLK3-SERPINA3 EGFR AKR7A2 0.848

 6

 BMP1 AKR7A2 CRP 0.848

 7

 C9 BMPER AKR7A2 0.848

 8

 C9 BMPER NME2 0.848

 9

 KLK3-SERPINA3 BMP1 AKR7A2 0.847

 10

 C9 AKR7A2 CRP 0.847

 eleven

 KLK3-SERPINA3 BMP1 NME2 0.847

 12

 BDNF KIT C9 0.846

 13

 BDNF C9 AKR7A2 0.845

 14

 KLK3-SERPINA3 EGFR NME2 0.845

 fifteen

 BMPER NME2 CRP 0.845

 16

 BMPER AKR7A2 CRP 0.845

 17

 KLK3-SERPINA3 BMPER AKR7A2 0.845

 18

 KLK3-SERPINA3 BDNF AKR7A2 0.844

 19

 KIT C9 AKR7A2 0.844

 twenty

 KLK3-SERPINA3 NME2 CRP 0.844

 twenty-one

 EGFR C9 AKR7A2 0.844

 22

 BDNF AKR7A2 CRP 0.844

 2. 3

 KLK3-SERPINA3 IGFBP4 AKR7A2 0.843

 24

 CNTN1 C9 AKR7A2 0.843

 25

 KLK3-SERPINA3 CA6 AKR7A2 0.843

 26

 C9 AHSG AKR7A2 0.843

 27

 KLK3-SERPINA3 IGFBP2 AKR7A2 0.843

 28

 KLK3-SERPINA3 BDNF KIT 0.842

 29

 KLK3-SERPINA3 C9 NME2 0.842

 30

 KLK3-SERPINA3 CNTN1 AKR7A2 0.842

 31

 KLK3-SERPINA3 BDNF NME2 0.841

 32

 BMP1 NME2 CRP 0.841

 33

 KLK3-SERPINA3 KIT AKR7A2 0.841

 3. 4

 AKR7A2 CRP 0.841 KIT

 35

 BMPER NME2 ITIH4 0.840

 36

 EGFR AKR7A2 CRP 0.840

 37

 KLK3-SERPINA3 STX1A AKR7A2 0.840

 38

 IGFBP4 C9 AKR7A2 0.839

 39

 KLK3-SERPINA3 IGFBP4 NME2 0.839

 40

 KLK3-SERPINA3 CNTN1 NME2 0.839

 41

 C9 DDC AKR7A2 0.839

 42

 BDNF C9 NME2 0.839

 43

 GHR AKR7A2 CRP 0.839

 44

 C9 BMP1 AKR7A2 0.839

 Four. Five

 KLK3-SERPINA3 BDNF CNTN1 0.838

 46

 KLK3-SERPINA3 STX1A NME2 0.838

 47

 IGFBP2 C9 AKR7A2 0.838

 48

 GHR C9 AKR7A2 0.838

 49

 C9 AKR7A2 ITIH4 0.838

 fifty

 BMP1 BMPER NME2 0.837

 51

 BDNF CRP KIT 0.837

 52

 C9 STX1A AKR7A2 0.837

 53

 BDNF NME2 CRP 0.837

 54

 KLK3-SERPINA3 AKR7A2 ITIH4 0.837

 55

 C9 NME2 CRP 0.836

 56

 C9 NME2 ITIH4 0.836

 57

 BMP1 BMPER AKR7A2 0.836

 58

 KLK3-SERPINA3 BDNF C9 0.836

 59

 KLK3-SERPINA3 AHSG AKR7A2 0.836

 60

 KLK3-SERPINA3 CA6 NME2 0.835

 61

 KLK3-SERPINA3 GHR AKR7A2 0.835

 62

 KIT C9 NME2 0.835

 63

 KLK3-SERPINA3 CNTN1 BMP1 0.835

 64

 C9 AHSG NME2 0.835

 65

 BDNF KIT AKR7A2 0.835

 66

 KLK3-SERPINA3 IGFBP2 NME2 0.835

 67

 STX1A AKR7A2 CRP 0.835

 68

 KLK3-SERPINA3 KIT STX1A 0.835

 69

 KLK3-SERPINA3 NME2 ITIH4 0.835

 70

 KLK3-SERPINA3 SERPINA1 AKR7A2 0.834

 71

 IGFBP4 AKR7A2 CRP 0.834

 72

 IGFBP2 BMPER AKR7A2 0.834

 73

 EGFR C9 NME2 0.834

 74

 KLK3-SERPINA3 BDNF CRP 0.834

 75

 KLK3-SERPINA3 STX1A CRP 0.834

 76

 GHR BMPER AKR7A2 0.833

 77

 IGFBP2 NME2 CRP 0.833

 78

 KLK3-SERPINA3 CNTN1 BMPER 0.833

 79

 KLK3-SERPINA3 KIT BMP1 0.833

 80

 KLK3-SERPINA3 BDNF EGFR 0.833

 81

 CNTN1 C9 NME2 0.833

 82

 KLK3-SERPINA3 KIT NME2 0.833

 83

 KLK3-SERPINA3 BDNF STX1A 0.833

 84

 KLK3-SERPINA3 AHSG NME2 0.833

 85

 CNTN1 AKR7A2 CRP 0.833

 86

 C9 SERPINA1 AKR7A2 0.833

 87

 KLK3-SERPINA3 C9 STX1A 0.833

 88

 KLK3-SERPINA3 BDNF CA6 0.833

 89

 EGFR AKR7A2 ITIH4 0.833

 90

 KLK3-SERPINA3 KIT EGFR 0.833

 91

 C9 DDC NME2 0.833

 92

 KLK3-SERPINA3 DDC AKR7A2 0.833

 93

 CNTN1 BMP1 AKR7A2 0.832

 94

 AKR7A2 CRP ITIH4 0.832

 95

 KLK3-SERPINA3 EGFR ITIH4 0.832

 96

 CNTN1 BMPER AKR7A2 0.832

 97

 KLK3-SERPINA3 EGFR AHSG 0.832

 98

 KLK3-SERPINA3 BDNF IGFBP4 0.832

 99

 IGFBP4 SERPINA1 AKR7A2 0.832

 100

 SERPINA1 BMPER AKR7A2 0.832

Table 23: 4 biomarker panels

 Markers

 Average VC ABC

 one

 BDNF KIT AKR7A2 CRP 0.860

 2

 KLK3-SERPINA3 CNTN1 BMPER NME2 0.860

 3

 BDNF KIT C9 AKR7A2 0.859

 4

 KLK3-SERPINA3 BMP1 BMPER NME2 0.859

 5

 KIT BMP1 AKR7A2 CRP 0.859

 6

 KLK3-SERPINA3 BMP1 NME2 CRP 0.858

 7

 KLK3-SERPINA3 CNTN1 BMP1 NME2 0.858

 8

 KLK3-SERPINA3 EGFR AKR7A2 CRP 0.857

 9

 KLK3-SERPINA3 C9 BMPER AKR7A2 0.857

 10

 KLK3-SERPINA3 KIT C9 AKR7A2 0.857

 eleven

 KLK3-SERPINA3 BMP1 AKR7A2 CRP 0.857

 12

 KLK3-SERPINA3 IGFBP2 AKR7A2 CRP 0.857

 13

 C9 BMPER AKR7A2 CRP 0.857

 14

 KLK3-SERPINA3 IGFBP4 C9 AKR7A2 0.857

 fifteen

 GHR BMPER AKR7A2 CRP 0.857

 16

 CNTN1 C9 BMPER AKR7A2 0.857

 17

 BDNF IGFBP2 AKR7A2 CRP 0.857

 18

 KIT C9 AKR7A2 CRP 0.857

 19

 IGFBP2 BMPER AKR7A2 CRP 0.857

 twenty

 KLK3-SERPINA3 EGFR C9 AKR7A2 0.856

 twenty-one

 KLK3-SERPINA3 CNTN1 BMP1 AKR7A2 0.856

 22

 KLK3-SERPINA3 CNTN1 C9 AKR7A2 0.856

 2. 3

 KLK3-SERPINA3 IGFBP4 AKR7A2 CRP 0.856

 24

 KLK3-SERPINA3 C9 BMPER NME2 0.856

 25

 KLK3-SERPINA3 KIT BMP1 AKR7A2 0.856

 26

 KLK3-SERPINA3 BMPER NME2 CRP 0.856

 27

 GHR C9 BMPER AKR7A2 0.856

 28

 CNTN1 C9 BMPER NME2 0.856

 29

 GHR BMPER NME2 CRP 0.855

 30

 KLK3-SERPINA3 BDNF KIT AKR7A2 0.855

 31

 BDNF C9 AKR7A2 CRP 0.855

 32

 KLK3-SERPINA3 C9 AKR7A2 CRP 0.855

 33

 KLK3-SERPINA3 BDNF AKR7A2 CRP 0.855

 3. 4

 IGFBP2 BMPER NME2 CRP 0.855

 35

 KLK3-SERPINA3 CNTN1 BMPER AKR7A2 0.855

 36

 KLK3-SERPINA3 BMPER AKR7A2 CRP 0.855

 37

 BMP1 BMPER AKR7A2 CRP 0.855

 38

 KLK3-SERPINA3 EGFR BMPER NME2 0.855

 39

 CNTN1 C9 BMP1 AKR7A2 0.855

 40

 KLK3-SERPINA3 KIT AKR7A2 CRP 0.854

 41

 KLK3-SERPINA3 GHR BMPER NME2 0.854

 42

 KLK3-SERPINA3 IGFBP4 BMPER NME2 0.854

 43

 IGFBP2 C9 AKR7A2 CRP 0.854

 44

 KLK3-SERPINA3 IGFBP2 CNTN1 AKR7A2 0.854

 Four. Five

 KLK3-SERPINA3 BDNF C9 AKR7A2 0.854

 46

 GHR C9 BMPER NME2 0.854

 47

 KLK3-SERPINA3 BMPER NME2 ITIH4 0.854

 48

 IGFBP2 KIT AKR7A2 CRP 0.854

 49

 KLK3-SERPINA3 EGFR NME2 CRP 0.854

 fifty

 KIT C9 BMPER AKR7A2 0.854

 51

 KIT EGFR C9 AKR7A2 0.854

 52

 BMP1 BMPER NME2 CRP 0.854

 53

 KLK3-SERPINA3 IGFBP2 BMPER AKR7A2 0.853

 54

 EGFR C9 AHSG AKR7A2 0.853

 55

 KLK3-SERPINA3 EGFR NME2 ITIH4 0.853

 56

 IGFBP2 CNTN1 AKR7A2 CRP 0.853

 57

 C9 BMPER NME2 ITIH4 0.853

 58

 IGFBP2 BMP1 AKR7A2 CRP 0.853

 59

 KLK3-SERPINA3 CNTN1 AKR7A2 CRP 0.853

 60

 KLK3-SERPINA3 IGFBP4 C9 NME2 0.853

 61

 KLK3-SERPINA3 IGFBP2 BMPER NME2 0.853

 62

 KLK3-SERPINA3 IGFBP4 SERPINA1 AKR7A2 0.853

 63

 BDNF CNTN1 C9 AKR7A2 0.853

 64

 CNTN1 BMP1 AKR7A2 CRP 0.853

 65

 KLK3-SERPINA3 BDNF CNTN1 AKR7A2 0.853

 66

 BDNF KIT C9 NME2 0.853

 67

 KLK3-SERPINA3 CNTN1 C9 NME2 0.853

 68

 KLK3-SERPINA3 EGFR BMPER AKR7A2 0.853

 69

 KLK3-SERPINA3 IGFBP4 AKR7A2 ITIH4 0.853

 70

 KLK3-SERPINA3 IGFBP4 NME2 CRP 0.853

 71

 KLK3-SERPINA3 IGFBP4 BMP1 AKR7A2 0.852

 72

 EGFR C9 AKR7A2 ITIH4 0.852

 73

 EGFR C9 AKR7A2 CRP 0.852

 74

 KLK3-SERPINA3 KIT BMP1 NME2 0.852

 75

 KLK3-SERPINA3 KIT EGFR AKR7A2 0.852

 76

 KLK3-SERPINA3 EGFR AKR7A2 ITIH4 0.852

 77

 KLK3-SERPINA3 BDNF NME2 CRP 0.852

 78

 IGFBP4 C9 AKR7A2 ITIH4 0.852

 79

 KLK3-SERPINA3 GHR BMPER AKR7A2 0.852

 80

 KLK3-SERPINA3 BMP1 BMPER AKR7A2 0.852

 81

 IGFBP2 C9 BMPER AKR7A2 0.852

 82

 BDNF KIT NME2 CRP 0.852

 83

 KLK3-SERPINA3 KIT C9 NME2 0.852

 84

 IGFBP2 AKR7A2 CRP ITIH4 0.852

 85

 C9 BMPER AKR7A2 ITIH4 0.852

 86

 KLK3-SERPINA3 EGFR BMP1 AKR7A2 0.852

 87

 KLK3-SERPINA3 C9 CA6 AKR7A2 0.852

 88

 KLK3-SERPINA3 NME2 CRP ITIH4 0.852

 89

 EGFR CNTN1 C9 AKR7A2 0.852

 90

 KLK3-SERPINA3 C9 STX1A AKR7A2 0.852

 91

 C9 BMPER NME2 CRP 0.852

 92

 KIT CNTN1 C9 AKR7A2 0.852

 93

 KLK3-SERPINA3 IGFBP4 BMPER AKR7A2 0.851

 94

 KIT C9 BMP1 AKR7A2 0.851

 95

 KLK3-SERPINA3 KIT BMPER NME2 0.851

 96

 KLK3-SERPINA3 CNTN1 NME2 CRP 0.851

 97

 KLK3-SERPINA3 BDNF KIT NME2 0.851

 98

 BDNF C9 AHSG AKR7A2 0.851

 99

 KLK3-SERPINA3 BDNF EGFR AKR7A2 0.851

 100

 KIT C9 BMPER NME2 0.851

Table 24: 5 biomarker panels

 Markers

 Average VC ABC

 one

 KLK3-SERPINA3 CNTN1 C9 BMPER AKR7A2 0.866

 2

 BDNF KIT C9 AKR7A2 CRP 0.866

 3

 KLK3-SERPINA3 CNTN1 BMP1 BMPER NME2 0.865

 4

 KLK3-SERPINA3 IGFBP2 CNTN1 AKR7A2 CRP 0.865

 5

 KLK3-SERPINA3 IGFBP2 CNTN1 BMPER AKR7A2 0.865

 6

 BDNF IGFBP2 KIT AKR7A2 CRP 0.865

 7

 KLK3-SERPINA3 BDNF KIT AKR7A2 CRP 0.865

 8

 KLK3-SERPINA3 IGFBP2 CNTN1 BMPER NME2 0.865

 9

 KLK3-SERPINA3 CNTN1 BMP1 NME2 CRP 0.865

 10

 KLK3-SERPINA3 KIT CNTN1 BMP1 AKR7A2 0.864

 eleven

 KLK3-SERPINA3 KIT C9 BMPER AKR7A2 0.864

 12

 KLK3-SERPINA3 KIT BMP1 AKR7A2 CRP 0.864

 13

 BDNF BMP1 KIT AKR7A2 CRP 0.864

 14

 KLK3-SERPINA3 KIT CNTN1 BMP1 NME2 0.864

 fifteen

 KLK3-SERPINA3 KIT C9 BMPER NME2 0.864

 16

 GHR C9 BMPER AKR7A2 CRP 0.864

 17

 KLK3-SERPINA3 EGFR NME2 CRP ITIH4 0.864

 18

 KLK3-SERPINA3 KIT BMP1 BMPER NME2 0.864

 19

 KLK3-SERPINA3 KIT CNTN1 C9 AKR7A2 0.864

 twenty

 KLK3-SERPINA3 BDNF KIT C9 AKR7A2 0.864

 twenty-one

 KLK3-SERPINA3 IGFBP4 C9 BMPER AKR7A2 0.863

 22

 GHR C9 BMPER AKR7A2 0.863 KIT

 2. 3

 KLK3-SERPINA3 CNTN1 BMPER AKR7A2 CRP 0.863

 24

 KLK3-SERPINA3 BDNF KIT CNTN1 AKR7A2 0.863

 25

 KLK3-SERPINA3 KIT IGFBP4 C9 AKR7A2 0.863

 26

 KLK3-SERPINA3 CNTN1 BMP1 AKR7A2 CRP 0.863

 27

 KLK3-SERPINA3 C9 BMPER AKR7A2 ITIH4 0.863

 28

 KIT BMP1 BMPER AKR7A2 CRP 0.863

 29

 KIT CNTN1 C9 BMP1 AKR7A2 0.863

 30

 KLK3-SERPINA3 KIT CNTN1 BMPER NME2 0.863

 31

 KLK3-SERPINA3 IGFBP2 BMPER AKR7A2 CRP 0.863

 32

 KLK3-SERPINA3 CNTN1 C9 BMPER NME2 0.863

 33

 KIT C9 BMPER AKR7A2 CRP 0.863

 3. 4

 KLK3-SERPINA3 CNTN1 BMP1 BMPER AKR7A2 0.863

 35

 KLK3-SERPINA3 IGFBP4 CNTN1 C9 AKR7A2 0.862

 36

 GHR BMPER AKR7A2 CRP 0.862 KIT

 37

 GHR CNTN1 C9 BMPER AKR7A2 0.862

 38

 KLK3-SERPINA3 CNTN1 BMPER NME2 CRP 0.862

 39

 KLK3-SERPINA3 GHR BMPER AKR7A2 CRP 0.862

 40

 BDNF KIT CNTN1 C9 AKR7A2 0.862

 41

 KLK3-SERPINA3 C9 BMPER AKR7A2 CRP 0.862

 42

 KLK3-SERPINA3 GHR C9 BMPER AKR7A2 0.862

 43

 KLK3-SERPINA3 IGFBP4 C9 AKR7A2 ITIH4 0.862

 44

 KLK3-SERPINA3 CNTN1 C9 BMP1 AKR7A2 0.862

 Four. Five

 KLK3-SERPINA3 KIT CNTN1 C9 NME2 0.862

 46

 IGFBP2 CNTN1 C9 BMPER AKR7A2 0.862

 47

 IGFBP2 CNTN1 BMPER AKR7A2 CRP 0.862

 48

 KLK3-SERPINA3 IGFBP2 KIT AKR7A2 CRP 0.862

 49

 KLK3-SERPINA3 IGFBP4 BMP1 NME2 CRP 0.862

 fifty

 KLK3-SERPINA3 IGFBP4 BMP1 AKR7A2 CRP 0.862

 51

 GHR KIT BMP1 AKR7A2 CRP 0.862

 52

 IGFBP2 C9 AKR7A2 CRP 0.862 KIT

 53

 KLK3-SERPINA3 BDNF CNTN1 C9 AKR7A2 0.862

 54

 KLK3-SERPINA3 IGFBP2 BMPER NME2 CRP 0.862

 55

 KLK3-SERPINA3 EGFR AKR7A2 CRP ITIH4 0.862

 56

 KLK3-SERPINA3 EGFR CNTN1 C9 AKR7A2 0.862

 57

 KLK3-SERPINA3 KIT BMP1 NME2 CRP 0.861

 58

 KLK3-SERPINA3 IGFBP4 BMPER AKR7A2 CRP 0.861

 59

 KLK3-SERPINA3 KIT C9 AKR7A2 CRP 0.861

 60

 KLK3-SERPINA3 KIT EGFR AKR7A2 CRP 0.861

 61

 KLK3-SERPINA3 IGFBP4 C9 BMPER NME2 0.861

 62

 KLK3-SERPINA3 KIT C9 BMP1 AKR7A2 0.861

 63

 GHR C9 AKR7A2 CRP 0.861 KIT

 64

 KLK3-SERPINA3 C9 DDC BMPER AKR7A2 0.861

 65

 KLK3-SERPINA3 IGFBP2 CNTN1 NME2 CRP 0.861

 66

 KIT CNTN1 C9 BMPER AKR7A2 0.861

 67

 KLK3-SERPINA3 KIT EGFR C9 AKR7A2 0.861

 68

 KLK3-SERPINA3 CNTN1 BMPER AKR7A2 ITIH4 0.861

 69

 KLK3-SERPINA3 EGFR C9 BMPER AKR7A2 0.861

 70

 CNTN1 C9 BMPER AKR7A2 CRP 0.861

 71

 GHR BMPER NME2 CRP 0.861 KIT

 72

 IGFBP2 C9 BMPER AKR7A2 CRP 0.861

 73

 KLK3-SERPINA3 GHR BMPER NME2 CRP 0.861

 74

 KLK3-SERPINA3 IGFBP2 CNTN1 C9 AKR7A2 0.861

 75

 BDNF IGFBP2 CNTN1 AKR7A2 CRP 0.861

 76

 IGFBP2 CNTN1 BMP1 AKR7A2 CRP 0.861

 77

 BDNF KIT C9 BMPER AKR7A2 0.861

 78

 KLK3-SERPINA3 BDNF C9 AKR7A2 CRP 0.861

 79

 IGFBP2 BMP1 AKR7A2 CRP 0.861 KIT

 80

 KLK3-SERPINA3 BMP1 BMPER NME2 CRP 0.861

 81

 KLK3-SERPINA3 BDNF IGFBP2 AKR7A2 CRP 0.861

 82

 KLK3-SERPINA3 IGFBP2 BMP1 AKR7A2 CRP 0.861

 83

 BDNF KIT GHR AKR7A2 CRP 0.861

 84

 KLK3-SERPINA3 IGFBP4 BMPER NME2 ITIH4 0.861

 85

 KLK3-SERPINA3 KIT BMPER NME2 CRP 0.861

 86

 KLK3-SERPINA3 IGFBP2 AKR7A2 CRP ITIH4 0.861

 87

 KLK3-SERPINA3 BMPER KIT AKR7A2 CRP 0.861

 88

 BDNF KIT C9 AHSG AKR7A2 0.860

 89

 IGFBP2 BMPER NME2 CRP ITIH4 0.860

 90

 IGFBP2 BMPER AKR7A2 CRP 0.860 KIT

 91

 KLK3-SERPINA3 IGFBP4 BMPER NME2 CRP 0.860

 92

 KLK3-SERPINA3 IGFBP4 KIT AKR7A2 CRP 0.860

 93

 KLK3-SERPINA3 IGFBP2 EGFR AKR7A2 CRP 0.860

 94

 KLK3-SERPINA3 IGFBP4 CNTN1 C9 NME2 0.860

 95

 KLK3-SERPINA3 GHR CNTN1 BMPER NME2 0.860

 96

 KLK3-SERPINA3 IGFBP4 C9 AKR7A2 CRP 0.860

 97

 KLK3-SERPINA3 KIT CNTN1 BMPER AKR7A2 0.860

 98

 KIT C9 BMP1 AKR7A2 CRP 0.860

 99

 KLK3-SERPINA3 IGFBP2 C9 BMPER AKR7A2 0.860

 100

 KLK3-SERPINA3 EGFR C9 AKR7A2 CRP 0.860

Table 25: 6 biomarker panels

 Markers

 Average VC ABC

 one

 KLK3-SERPINA3 AKR7A2 KIT CNTN1 C9 BMPER 0.871

 2

 KIT CRP GHR C9 BMPER AKR7A2 0.871

 3

 KLK3-SERPINA3 AKR7A2 BDNF KIT CNTN1 C9 0.871

 4

 KLK3-SERPINA3 AKR7A2 KIT CNTN1 C9 BMP1 0.871

 5

 KLK3-SERPINA3 CRP IGFBP2 CNTN1 BMPER AKR7A2 0.871

 6

 KLK3-SERPINA3 AKR7A2 IGFBP4 CNTN1 C9 BMPER 0.871

 7

 KLK3-SERPINA3 NME2 KIT CNTN1 C9 BMPER 0.870

 8

 KLK3-SERPINA3 AKR7A2 KIT GHR C9 BMPER 0.870

 9

 KLK3-SERPINA3 AKR7A2 IGFBP2 CNTN1 C9 BMPER 0.870

 10

 KLK3-SERPINA3 ITIH4 IGFBP2 CNTN1 BMPER AKR7A2 0.870

 eleven

 KLK3-SERPINA3 AKR7A2 KIT IGFBP4 C9 BMPER 0.870

 12

 KLK3-SERPINA3 NME2 KIT CNTN1 BMP1 BMPER 0.870

 13

 BDNF CRP KIT IGFBP2 C9 AKR7A2 0.869

 14

 BDNF CRP KIT GHR C9 AKR7A2 0.869

 fifteen

 KLK3-SERPINA3 CRP IGFBP2 CNTN1 BMP1 AKR7A2 0.869

 16

 KLK3-SERPINA3 CRP KIT CNTN1 BMP1 NME2 0.869

 17

 KLK3-SERPINA3 AKR7A2 KIT CNTN1 BMP1 BMPER 0.869

 18

 BDNF CRP KIT IGFBP2 CNTN1 AKR7A2 0.869

 19

 KIT CRP GHR BMP1 BMPER AKR7A2 0.869

 twenty

 KLK3-SERPINA3 CRP IGFBP2 CNTN1 BMPER NME2 0.869

 twenty-one

 KLK3-SERPINA3 AKR7A2 CNTN1 C9 BMP1 BMPER 0.868

 22

 KLK3-SERPINA3 ITIH4 IGFBP4 C9 BMPER AKR7A2 0.868

 2. 3

 KLK3-SERPINA3 AKR7A2 KIT IGFBP2 CNTN1 BMPER 0.868

 24

 GHR CRP CNTN1 C9 BMPER AKR7A2 0.868

 25

 KIT AKR7A2 GHR CNTN1 C9 BMPER 0.868

 26

 KLK3-SERPINA3 AKR7A2 GHR CNTN1 C9 BMPER 0.868

 27

 KLK3-SERPINA3 ITIH4 KIT IGFBP4 C9 AKR7A2 0.868

 28

 KLK3-SERPINA3 ITIH4 IGFBP4 CNTN1 BMPER AKR7A2 0.868

 29

 BDNF AKR7A2 KIT IGFBP2 CNTN1 C9 0.867

 30

 KIT AKR7A2 CNTN1 C9 BMP1 BMPER 0.867

 31

 KLK3-SERPINA3 CRP BDNF KIT IGFBP2 AKR7A2 0.867

 32

 KLK3-SERPINA3 ITIH4 CNTN1 BMP1 BMPER AKR7A2 0.867

 33

 KLK3-SERPINA3 CRP KIT GHR BMPER NME2 0.867

 3. 4

 KLK3-SERPINA3 CRP BDNF KIT C9 AKR7A2 0.867

 35

 KLK3-SERPINA3 NME2 KIT CNTN1 C9 BMP1 0.867

 36

 KLK3-SERPINA3 CRP BDNF KIT CNTN1 AKR7A2 0.867

 37

 KLK3-SERPINA3 ITIH4 IGFBP4 CNTN1 C9 AKR7A2 0.867

 38

 KLK3-SERPINA3 ITIH4 CNTN1 C9 BMPER AKR7A2 0.867

 39

 KLK3-SERPINA3 NME2 BDNF KIT CNTN1 C9 0.867

 40

 KLK3-SERPINA3 CRP KIT CNTN1 BMP1 AKR7A2 0.867

 41

 KLK3-SERPINA3 CRP BDNF IGFBP2 CNTN1 AKR7A2 0.867

 42

 KLK3-SERPINA3 CRP KIT IGFBP2 CNTN1 AKR7A2 0.867

 43

 KLK3-SERPINA3 ITIH4 IGFBP4 CNTN1 BMPER NME2 0.867

 44

 KLK3-SERPINA3 CRP GHR CNTN1 BMPER AKR7A2 0.867

 Four. Five

 KLK3-SERPINA3 AKR7A2 EGFR CNTN1 C9 BMPER 0.867

 46

 KLK3-SERPINA3 CRP KIT IGFBP2 BMPER AKR7A2 0.867

 47

 CRP KIT IGFBP2 C9 BMPER AKR7A2 0.867

 48

 KLK3-SERPINA3 AKR7A2 KIT EGFR C9 BMPER 0.867

 49

 KLK3-SERPINA3 NME2 KIT IGFBP2 CNTN1 BMPER 0.867

 fifty

 KLK3-SERPINA3 AKR7A2 KIT C9 BMP1 BMPER 0.867

 51

 KLK3-SERPINA3 CRP KIT GHR BMPER AKR7A2 0.867

 52

 KLK3-SERPINA3 CRP CNTN1 BMP1 BMPER AKR7A2 0.867

 53

 BDNF CRP KIT C9 BMPER AKR7A2 0.867

 54

 CRP GHR KIT BMP1 BMPER NME2 0.867

 55

 KLK3-SERPINA3 ITIH4 IGFBP2 BMPER AKR7A2 CRP 0.867

 56

 KIT AKR7A2 IGFBP2 CNTN1 C9 BMPER 0.867

 57

 KLK3-SERPINA3 ITIH4 IGFBP4 CNTN1 BMP1 AKR7A2 0.867

 58

 KLK3-SERPINA3 AKR7A2 KIT IGFBP4 CNTN1 C9 0.867

 59

 KLK3-SERPINA3 ITIH4 IGFBP2 CNTN1 BMPER NME2 0.867

 60

 IGFBP2 CRP CNTN1 C9 BMPER AKR7A2 0.866

 61

 KLK3-SERPINA3 AKR7A2 KIT EGFR CNTN1 C9 0.866

 62

 KLK3-SERPINA3 CRP KIT IGFBP2 BMP1 AKR7A2 0.866

 63

 BDNF CRP KIT CNTN1 C9 AKR7A2 0.866

 64

 KLK3-SERPINA3 CRP KIT C9 BMPER AKR7A2 0.866

 65

 KLK3-SERPINA3 ITIH4 IGFBP4 CNTN1 BMP1 NME2 0.866

 66

 KLK3-SERPINA3 ITIH4 IGFBP4 BMP1 AKR7A2 CRP 0.866

 67

 KLK3-SERPINA3 CRP KIT IGFBP4 BMP1 AKR7A2 0.866

 68

 KLK3-SERPINA3 CRP GHR C9 BMPER AKR7A2 0.866

 69

 KLK3-SERPINA3 CRP KIT BMP1 BMPER AKR7A2 0.866

 70

 KLK3-SERPINA3 AKR7A2 IGFBP2 CNTN1 BMP1 BMPER 0.866

 71

 KLK3-SERPINA3 AKR7A2 IGFBP2 CNTN1 DDC BMPER 0.866

 72

 KLK3-SERPINA3 AKR7A2 KIT IGFBP2 CNTN1 C9 0.866

 73

 KLK3-SERPINA3 AKR7A2 KIT C9 DDC BMPER 0.866

 74

 KLK3-SERPINA3 CRP KIT CNTN1 BMPER AKR7A2 0.866

 75

 KLK3-SERPINA3 CRP IGFBP4 CNTN1 BMP1 AKR7A2 0.866

 76

 KLK3-SERPINA3 AKR7A2 KIT IGFBP4 CNTN1 BMP1 0.866

 77

 KLK3-SERPINA3 CRP BDNF KIT CNTN1 NME2 0.866

 78

 KLK3-SERPINA3 ITIH4 CNTN1 BMP1 AKR7A2 CRP 0.866

 79

 CRP KIT IGFBP2 CNTN1 BMP1 AKR7A2 0.866

 80

 KLK3-SERPINA3 CRP IGFBP2 CNTN1 BMP1 NME2 0.866

 81

 KLK3-SERPINA3 CRP CNTN1 C9 BMPER AKR7A2 0.866

 82

 KLK3-SERPINA3 AKR7A2 BDNF KIT CNTN1 BMP1 0.866

 83

 KLK3-SERPINA3 CRP IGFBP4 CNTN1 BMP1 NME2 0.866

 84

 KLK3-SERPINA3 ITIH4 IGFBP4 BMPER AKR7A2 CRP 0.866

 85

 KLK3-SERPINA3 CRP IGFBP2 EGFR CNTN1 AKR7A2 0.866

 86

 BDNF CRP KIT C9 AHSG AKR7A2 0.866

 87

 KLK3-SERPINA3 AKR7A2 KIT IGFBP2 CNTN1 BMP1 0.866

 88

 KLK3-SERPINA3 CRP KIT BMP1 BMPER NME2 0.866

 89

 KLK3-SERPINA3 CRP BDNF CNTN1 C9 AKR7A2 0.866

 90

 KLK3-SERPINA3 ITIH4 KIT CNTN1 BMPER NME2 0.866

 91

 KLK3-SERPINA3 ITIH4 IGFBP4 BMPER NME2 CRP 0.866

 92

 CRP IGFBP2 CNTN1 BMPER AKR7A2 0.866 KIT

 93

 KLK3-SERPINA3 NME2 IGFBP4 CNTN1 C9 BMPER 0.866

 94

 KLK3-SERPINA3 AKR7A2 IGFBP4 CNTN1 C9 BMP1 0.866

 95

 KLK3-SERPINA3 CRP KIT CNTN1 BMPER NME2 0.866

 96

 KLK3-SERPINA3 ITIH4 IGFBP2 CNTN1 AKR7A2 CRP 0.866

 97

 KLK3-SERPINA3 AKR7A2 BDNF KIT C9 BMPER 0.866

 98

 KLK3-SERPINA3 CRP GHR IGFBP4 BMPER AKR7A2 0.866

 99

 BDNF CRP KIT IGFBP2 AHSG AKR7A2 0.866

 100

 KLK3-SERPINA3 ITIH4 KIT C9 BMPER AKR7A2 0.866

Table 26: 7 biomarker panels

 Markers

 Average VC ABC

 one

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CNTN1 C9 0.875

 2

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CNTN1 C9 0.875

 3

 KLK3-SERPINA3 BMPER KIT AKR7A2 CNTN1 C9 BMP1 0.875

 4

 KLK3-SERPINA3 AKR7A2 CRP IGFBP2 KIT CNTN1 BMPER 0.874

 5

 KLK3-SERPINA3 AKR7A2 KIT ITIH4 CNTN1 BMP1 BMPER 0.873

 6

 KLK3-SERPINA3 AKR7A2 IGFBP4 ITIH4 CNTN1 C9 BMPER 0.873

 7

 KLK3-SERPINA3 AKR7A2 BDNF CRP KIT IGFBP2 CNTN1 0.873

 8

 KIT AKR7A2 GHR CRP CNTN1 C9 BMPER 0.873

 9

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 CNTN1 C9 0.873

 10

 KLK3-SERPINA3 AKR7A2 KIT ITIH4 IGFBP4 C9 BMPER 0.873

 eleven

 KLK3-SERPINA3 BMPER BDNF AKR7A2 KIT CNTN1 C9 0.872

 12

 KLK3-SERPINA3 AKR7A2 CRP IGFBP2 KIT CNTN1 BMP1 0.872

 13

 KLK3-SERPINA3 AKR7A2 CRP GHR KIT CNTN1 BMPER 0.872

 14

 KLK3-SERPINA3 AKR7A2 IGFBP4 ITIH4 CNTN1 BMP1 BMPER 0.872

 fifteen

 KIT AKR7A2 GHR CRP CNTN1 BMP1 BMPER 0.872

 16

 KLK3-SERPINA3 CRP IGFBP2 ITIH4 CNTN1 BMPER AKR7A2 0.872

 17

 KLK3-SERPINA3 AKR7A2 CRP GHR C9 BMPER KIT 0.872

 18

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR IGFBP4 C9 0.872

 19

 KLK3-SERPINA3 AKR7A2 CRP KIT CNTN1 BMP1 BMPER 0.872

 twenty

 KLK3-SERPINA3 BMPER KIT NME2 GHR CNTN1 C9 0.872

 twenty-one

 KLK3-SERPINA3 AKR7A2 BDNF CRP KIT CNTN1 C9 0.872

 22

 KLK3-SERPINA3 NME2 KIT ITIH4 IGFBP4 CNTN1 BMP1 0.872

 2. 3

 KLK3-SERPINA3 AKR7A2 KIT ITIH4 IGFBP2 CNTN1 BMPER 0.872

 24

 KLK3-SERPINA3 AKR7A2 IGFBP2 ITIH4 IGFBP4 CNTN1 BMPER 0.872

 25

 KLK3-SERPINA3 CRP IGFBP4 ITIH4 CNTN1 BMP1 AKR7A2 0.872

 26

 KIT AKR7A2 GHR CRP C9 BMP1 BMPER 0.872

 27

 KLK3-SERPINA3 BMPER IGFBP4 AKR7A2 CNTN1 C9 BMP1 0.872

 28

 KLK3-SERPINA3 AKR7A2 IGFBP2 CRP GHR CNTN1 BMPER 0.872

 29

 KIT BMPER GHR AKR7A2 CNTN1 C9 BMP1 0.872

 30

 KLK3-SERPINA3 BMPER KIT AKR7A2 CNTN1 C9 AHSG 0.872

 31

 KLK3-SERPINA3 AKR7A2 IGFBP2 ITIH4 CNTN1 DDC BMPER 0.872

 32

 KLK3-SERPINA3 AKR7A2 KIT CRP GHR IGFBP4 BMPER 0.872

 33

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CNTN1 BMP1 0.872

 3. 4

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CNTN1 BMP1 0.872

 35

 KLK3-SERPINA3 AKR7A2 CRP GHR KIT BMP1 BMPER 0.871

 36

 KLK3-SERPINA3 BMPER GHR AKR7A2 IGFBP4 CNTN1 C9 0.871

 37

 BDNF AKR7A2 CRP IGFBP2 KIT CNTN1 C9 0.871

 38

 BDNF AKR7A2 KIT CRP GHR C9 BMPER 0.871

 39

 KIT AKR7A2 IGFBP2 CRP CNTN1 C9 BMPER 0.871

 40

 KLK3-SERPINA3 NME2 CRP IGFBP2 CNTN1 BMPER KIT 0.871

 41

 KLK3-SERPINA3 BMP1 KIT AKR7A2 IGFBP4 CNTN1 C9 0.871

 42

 KIT AKR7A2 GHR CRP C9 AHSG BMPER 0.871

 43

 KLK3-SERPINA3 AKR7A2 IGFBP2 ITIH4 CNTN1 C9 BMPER 0.871

 44

 KLK3-SERPINA3 AKR7A2 CRP GHR KIT CNTN1 BMP1 0.871

 Four. Five

 KLK3-SERPINA3 AKR7A2 KIT ITIH4 IGFBP4 CNTN1 BMP1 0.871

 46

 KLK3-SERPINA3 AKR7A2 IGFBP2 CRP IGFBP4 CNTN1 BMPER 0.871

 47

 KLK3-SERPINA3 AKR7A2 IGFBP2 CRP CNTN1 C9 BMPER 0.871

 48

 KLK3-SERPINA3 CRP GHR ITIH4 IGFBP4 BMPER AKR7A2 0.871

 49

 KLK3-SERPINA3 AKR7A2 KIT ITIH4 GHR CNTN1 BMPER 0.871

 fifty

 KLK3-SERPINA3 CRP IGFBP2 ITIH4 CNTN1 BMP1 AKR7A2 0.871

 51

 KLK3-SERPINA3 CRP IGFBP2 ITIH4 IGFBP4 BMPER AKR7A2 0.871

 52

 KLK3-SERPINA3 AKR7A2 KIT ITIH4 IGFBP4 CNTN1 BMPER 0.871

 53

 KLK3-SERPINA3 AKR7A2 KIT ITIH4 CNTN1 C9 BMPER 0.871

 54

 KLK3-SERPINA3 AKR7A2 IGFBP2 CRP CNTN1 BMP1 BMPER 0.871

 55

 KLK3-SERPINA3 NME2 CRP IGFBP2 KIT CNTN1 BMP1 0.871

 56

 KLK3-SERPINA3 AKR7A2 KIT ITIH4 IGFBP4 CNTN1 C9 0.871

 57

 KLK3-SERPINA3 BMPER KIT AKR7A2 EGFR CNTN1 C9 0.870

 58

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 C9 BMP1 0.870

 59

 KLK3-SERPINA3 AKR7A2 IGFBP2 ITIH4 CNTN1 BMP1 BMPER 0.870

 60

 KLK3-SERPINA3 AKR7A2 GHR ITIH4 IGFBP4 CNTN1 BMPER 0.870

 61

 KLK3-SERPINA3 BMP1 BDNF AKR7A2 KIT CNTN1 C9 0.870

 62

 KIT AKR7A2 IGFBP2 CRP CNTN1 BMP1 BMPER 0.870

 63

 KLK3-SERPINA3 AKR7A2 BDNF CRP KIT CNTN1 BMP1 0.870

 64

 KLK3-SERPINA3 AKR7A2 GHR CRP CNTN1 C9 BMPER 0.870

 65

 KLK3-SERPINA3 NME2 IGFBP4 ITIH4 CNTN1 BMP1 BMPER 0.870

 66

 KLK3-SERPINA3 AKR7A2 CRP IGFBP2 C9 BMPER KIT 0.870

 67

 KLK3-SERPINA3 C9 BDNF AKR7A2 IGFBP2 KIT CNTN1 0.870

 68

 KLK3-SERPINA3 CRP KIT ITIH4 IGFBP4 BMP1 AKR7A2 0.870

 69

 KIT AKR7A2 GHR CRP CNTN1 C9 BMP1 0.870

 70

 KLK3-SERPINA3 DDC KIT AKR7A2 CNTN1 C9 BMP1 0.870

 71

 KLK3-SERPINA3 AKR7A2 CRP KIT CNTN1 C9 BMPER 0.870

 72

 KLK3-SERPINA3 AKR7A2 CRP GHR KIT IGFBP4 C9 0.870

 73

 KLK3-SERPINA3 AKR7A2 KIT CRP GHR IGFBP4 BMP1 0.870

 74

 KLK3-SERPINA3 AKR7A2 BDNF CRP KIT C9 BMPER 0.870

 75

 KLK3-SERPINA3 NME2 CRP KIT CNTN1 BMP1 BMPER 0.870

 76

 KLK3-SERPINA3 CRP IGFBP2 ITIH4 IGFBP4 CNTN1 AKR7A2 0.870

 77

 KLK3-SERPINA3 AKR7A2 GHR CRP CNTN1 BMP1 BMPER 0.870

 78

 KLK3-SERPINA3 BMPER KIT NME2 CNTN1 C9 BMP1 0.870

 79

 KLK3-SERPINA3 AKR7A2 CRP IGFBP2 GHR BMPER KIT 0.870

 80

 KLK3-SERPINA3 AKR7A2 CRP EGFR KIT CNTN1 BMP1 0.870

 81

 KLK3-SERPINA3 AKR7A2 CRP IGFBP4 KIT CNTN1 BMP1 0.870

 82

 KIT AKR7A2 EGFR CRP GHR C9 BMPER 0.870

 83

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR C9 AHSG 0.870

 84

 KLK3-SERPINA3 AKR7A2 BDNF CRP IGFBP2 CNTN1 C9 0.870

 85

 KIT AKR7A2 IGFBP2 CRP GHR C9 BMPER 0.870

 86

 KLK3-SERPINA3 CRP KIT ITIH4 IGFBP4 BMPER AKR7A2 0.870

 87

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR C9 BMP1 0.870

 88

 KLK3-SERPINA3 AKR7A2 CRP KIT CNTN1 C9 BMP1 0.870

 89

 KLK3-SERPINA3 BMP1 KIT AKR7A2 EGFR CNTN1 C9 0.870

 90

 KLK3-SERPINA3 AKR7A2 ITIH4 KIT EGFR C9 BMPER 0.870

 91

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CNTN1 DDC 0.870

 92

 BDNF AKR7A2 CRP IGFBP2 C9 BMPER KIT 0.870

 93

 KLK3-SERPINA3 BMPER IGFBP2 AKR7A2 IGFBP4 CNTN1 C9 0.870

 94

 KLK3-SERPINA3 BMPER KIT AKR7A2 CNTN1 C9 CA6 0.870

 95

 KLK3-SERPINA3 CRP KIT ITIH4 GHR IGFBP4 AKR7A2 0.870

 96

 KLK3-SERPINA3 AKR7A2 CRP GHR KIT CNTN1 C9 0.870

 97

 KLK3-SERPINA3 BMPER IGFBP2 AKR7A2 CNTN1 C9 DDC 0.870

 98

 KLK3-SERPINA3 BMPER KIT AKR7A2 CNTN1 C9 DDC 0.870

 99

 KLK3-SERPINA3 NME2 IGFBP4 ITIH4 CNTN1 C9 BMPER 0.870

 100

 KIT AKR7A2 CNTN1 CRP C9 BMP1 BMPER 0.870

 Markers

 Average VC ABC

 one

 KLK3-SERPINA3 BMP1 BMPER KIT GHR AKR7A2 CNTN1 C9 0.877

 2

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 ITIH4 CNTN1 BMP1 0.876

 3

 KLK3-SERPINA3 AKR7A2 CRP GHR KIT ITIH4 IGFBP4 BMPER 0.876

 4

 KLK3-SERPINA3 C9 BMPER KIT GHR AKR7A2 IGFBP4 CNTN1 0.876

 5

 KLK3-SERPINA3 AHSG KIT BMPER GHR AKR7A2 CNTN1 C9 0.876

 6

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP CNTN1 BMP1 0.876

 7

 KLK3-SERPINA3 AKR7A2 IGFBP2 CRP IGFBP4 ITIH4 CNTN1 BMPER 0.876

 8

 KIT BMPER GHR AKR7A2 CNTN1 CRP C9 BMP1 0.876

 9

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP GHR CNTN1 0.876

 10

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP CNTN1 BMP1 0.876

 eleven

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP CNTN1 C9 0.875

 12

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 ITIH4 CNTN1 C9 0.875

 13

 KLK3-SERPINA3 BMP1 BMPER KIT IGFBP4 AKR7A2 CNTN1 C9 0.875

 14

 KLK3-SERPINA3 AHSG BMPER KIT IGFBP2 AKR7A2 CNTN1 C9 0.875

 fifteen

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP CNTN1 C9 0.875

 16

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR ITIH4 CNTN1 BMP1 0.875

 17

 KLK3-SERPINA3 AKR7A2 CRP IGFBP4 ITIH4 KIT CNTN1 BMP1 0.875

 18

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR ITIH4 IGFBP4 C9 0.875

 19

 KLK3-SERPINA3 BMP1 KIT AKR7A2 GHR CRP IGFBP4 CNTN1 0.875

 twenty

 KLK3-SERPINA3 AHSG KIT BMPER CNTN1 AKR7A2 C9 BMP1 0.874

 twenty-one

 KLK3-SERPINA3 C9 BMPER KIT IGFBP2 AKR7A2 GHR CNTN1 0.874

 22

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP IGFBP4 C9 0.874

 2. 3

 KLK3-SERPINA3 BMP1 BMPER KIT IGFBP2 AKR7A2 CNTN1 C9 0.874

 24

 KLK3-SERPINA3 CA6 KIT BMPER GHR AKR7A2 CNTN1 C9 0.874

 25

 KLK3-SERPINA3 AKR7A2 CRP KIT IGFBP4 ITIH4 BMP1 BMPER 0.874

 26

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 ITIH4 CNTN1 BMP1 0.874

 27

 KLK3-SERPINA3 BMPER KIT NME2 IGFBP4 ITIH4 CNTN1 BMP1 0.874

 28

 KLK3-SERPINA3 BMPER KIT NME2 GHR CRP CNTN1 BMP1 0.874

 29

 KLK3-SERPINA3 AKR7A2 CRP IGFBP2 ITIH4 KIT CNTN1 BMPER 0.874

 30

 KLK3-SERPINA3 BMP1 KIT AKR7A2 GHR CRP CNTN1 C9 0.874

 31

 KLK3-SERPINA3 AKR7A2 KIT CRP GHR ITIH4 CNTN1 BMPER 0.874

 32

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP IGFBP4 CNTN1 0.874

 33

 KLK3-SERPINA3 AKR7A2 GHR CRP IGFBP4 ITIH4 CNTN1 BMP1 0.874

 3. 4

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 ITIH4 CNTN1 C9 0.874

 35

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR ITIH4 IGFBP4 CNTN1 0.874

 36

 KLK3-SERPINA3 BMPER IGFBP2 AKR7A2 IGFBP4 ITIH4 CNTN1 C9 0.874

 37

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 ITIH4 C9 BMP1 0.874

 38

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR ITIH4 CNTN1 C9 0.874

 39

 KIT BMPER IGFBP2 AKR7A2 GHR CRP CNTN1 C9 0.874

 40

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP IGFBP4 BMP1 0.874

 41

 KIT AKR7A2 GHR CRP IGFBP4 ITIH4 C9 BMPER 0.873

 42

 KLK3-SERPINA3 BMPER GHR AKR7A2 IGFBP4 ITIH4 CNTN1 C9 0.873

 43

 KLK3-SERPINA3 BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 C9 0.873

 44

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 CRP CNTN1 BMP1 0.873

 Four. Five

 KLK3-SERPINA3 AKR7A2 KIT CRP GHR ITIH4 BMP1 BMPER 0.873

 46

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 ITIH4 C9 AHSG 0.873

 47

 KLK3-SERPINA3 BMP1 KIT AKR7A2 IGFBP4 ITIH4 CNTN1 C9 0.873

 48

 KLK3-SERPINA3 BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 BMP1 0.873

 49

 BDNF BMPER KIT AKR7A2 GHR CRP CNTN1 C9 0.873

 fifty

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 ITIH4 C9 DDC 0.873

 51

 KLK3-SERPINA3 AKR7A2 CRP GHR KIT ITIH4 IGFBP4 BMP1 0.873

 52

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 ITIH4 CNTN1 DDC 0.873

 53

 KLK3-SERPINA3 BMPER KIT AKR7A2 CNTN1 ITIH4 BMP1 DDC 0.873

 54

 KLK3-SERPINA3 BMPER BDNF AKR7A2 CRP KIT CNTN1 C9 0.873

 55

 KLK3-SERPINA3 BMPER BDNF AKR7A2 CRP IGFBP2 KIT CNTN1 0.873

 56

 KIT BMPER GHR AKR7A2 CNTN1 CRP C9 FN1 0.873

 57

 KLK3-SERPINA3 AKR7A2 CRP KIT IGFBP2 ITIH4 IGFBP4 BMPER 0.873

 58

 KLK3-SERPINA3 AKR7A2 GHR CRP IGFBP4 ITIH4 CNTN1 BMPER 0.873

 59

 KLK3-SERPINA3 BMPER IGFBP4 AKR7A2 CNTN1 ITIH4 C9 AHSG 0.873

 60

 KLK3-SERPINA3 NME2 CRP IGFBP4 ITIH4 KIT CNTN1 BMP1 0.873

 61

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP C9 AHSG 0.873

 62

 KLK3-SERPINA3 BMP1 BDNF AKR7A2 CRP IGFBP2 KIT CNTN1 0.873

 63

 KLK3-SERPINA3 DDC BMPER KIT IGFBP2 AKR7A2 CNTN1 C9 0.873

 64

 KLK3-SERPINA3 AKR7A2 IGFBP2 CRP CNTN1 ITIH4 BMP1 BMPER 0.873

 65

 KLK3-SERPINA3 DDC KIT BMPER GHR AKR7A2 CNTN1 C9 0.873

 66

 KIT AKR7A2 GHR CRP IGFBP4 ITIH4 BMP1 BMPER 0.873

 67

 KLK3-SERPINA3 AKR7A2 BDNF CRP KIT ITIH4 IGFBP2 CNTN1 0.873

 68

 KLK3-SERPINA3 C9 BMPER KIT IGFBP2 AKR7A2 IGFBP4 CNTN1 0.873

 69

 KLK3-SERPINA3 C9 BDNF AKR7A2 CRP IGFBP2 KIT CNTN1 0.873

 70

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 ITIH4 IGFBP4 CNTN1 0.873

 71

 KLK3-SERPINA3 AHSG BMPER KIT IGFBP4 AKR7A2 CNTN1 C9 0.873

 72

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP C9 BMP1 0.873

 73

 KLK3-SERPINA3 AKR7A2 IGFBP2 CRP IGFBP4 ITIH4 CNTN1 BMP1 0.873

 74

 KLK3-SERPINA3 BMP1 KIT NME2 GHR CRP IGFBP4 CNTN1 0.873

 75

 KLK3-SERPINA3 NME2 CRP GHR KIT ITIH4 IGFBP4 BMPER 0.873

 76

 KLK3-SERPINA3 DDC KIT AKR7A2 IGFBP2 CRP CNTN1 BMP1 0.873

 77

 KLK3-SERPINA3 AKR7A2 CRP KIT CNTN1 ITIH4 BMP1 BMPER 0.873

 78

 BDNF BMPER KIT AKR7A2 IGFBP2 CRP CNTN1 C9 0.873

 79

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP CNTN1 CA6 0.873

 80

 KLK3-SERPINA3 BMPER IGFBP4 AKR7A2 CNTN1 ITIH4 C9 BMP1 0.873

 81

 KLK3-SERPINA3 AKR7A2 CRP KIT EGFR ITIH4 CNTN1 BMP1 0.873

 82

 KLK3-SERPINA3 BMP1 KIT AKR7A2 IGFBP2 CRP CNTN1 C9 0.872

 83

 KLK3-SERPINA3 AKR7A2 CRP IGFBP2 ITIH4 KIT CNTN1 BMP1 0.872

 84

 KLK3-SERPINA3 BMPER KIT AKR7A2 CNTN1 ITIH4 C9 BMP1 0.872

 85

 KLK3-SERPINA3 AKR7A2 KIT CRP GHR ITIH4 IGFBP4 CNTN1 0.872

 86

 KLK3-SERPINA3 BMPER KIT AKR7A2 CNTN1 CRP C9 BMP1 0.872

 87

 KLK3-SERPINA3 AKR7A2 IGFBP4 CRP CNTN1 ITIH4 BMP1 BMPER 0.872

 88

 KLK3-SERPINA3 C9 BMPER KIT IGFBP2 AKR7A2 EGFR CNTN1 0.872

 89

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP CNTN1 AHSG 0.872

 90

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP GHR C9 0.872

 91

 KLK3-SERPINA3 AKR7A2 CRP GHR KIT ITIH4 IGFBP4 C9 0.872

 92

 KLK3-SERPINA3 AHSG KIT BMPER GHR AKR7A2 IGFBP4 C9 0.872

 93

 KLK3-SERPINA3 BMP1 KIT BMPER GHR AKR7A2 IGFBP4 C9 0.872

 94

 KLK3-SERPINA3 C9 BDNF BMPER KIT AKR7A2 IGFBP2 CNTN1 0.872

 95

 KIT BMPER GHR AKR7A2 CNTN1 CRP C9 AHSG 0.872

 96

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP CNTN1 AHSG 0.872

 97

 KIT BMPER IGFBP2 AKR7A2 GHR CRP CNTN1 BMP1 0.872

 98

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR ITIH4 CNTN1 CA6 0.872

 99

 KLK3-SERPINA3 FN1 KIT BMPER GHR AKR7A2 CNTN1 C9 0.872

 100

 KIT AHSG GHR BMPER CNTN1 AKR7A2 C9 BMP1 0.872

Table 28: Panels of 9 biomarkers

 Markers

 Average VC ABC

 one

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP IGFBP4 ITIH4 BMP1 0.878

 2

 KLK3-SERPINA3 BMP1 KIT AKR7A2 GHR CRP IGFBP4 ITIH4 CNTN1 0.878

 3

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP IGFBP4 ITIH4 CNTN1 0.878

 4

 KLK3-SERPINA3 BMP1 KIT BMPER GHR AKR7A2 IGFBP4 ITIH4 CNTN1 0.878

 5

 KLK3-SERPINA3 BMP1 BMPER GHR KIT AKR7A2 CNTN1 CRP C9 0.877

 6

 KLK3-SERPINA3 BMP1 BMPER KIT IGFBP4 AKR7A2 CNTN1 ITIH4 C9 0.877

 7

 KLK3-SERPINA3 BMP1 KIT AHSG GHR BMPER CNTN1 AKR7A2 C9 0.877

 8

 KLK3-SERPINA3 BMP1 BMPER GHR KIT AKR7A2 IGFBP4 CRP CNTN1 0.877

 9

 KLK3-SERPINA3 C9 KIT BMP1 GHR BMPER IGFBP4 AKR7A2 CNTN1 0.877

 10

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP IGFBP4 ITIH4 C9 0.877

 eleven

 KLK3-SERPINA3 C9 BMPER KIT GHR AKR7A2 IGFBP4 ITIH4 CNTN1 0.877

 12

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP IGFBP4 ITIH4 CNTN1 0.877

 13

 KLK3-SERPINA3 C9 BMPER KIT GHR AKR7A2 IGFBP4 CRP CNTN1 0.877

 14

 KLK3-SERPINA3 BMP1 BMPER KIT IGFBP2 AKR7A2 GHR CRP CNTN1 0.876

 fifteen

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP CNTN1 ITIH4 BMP1 0.876

 16

 KLK3-SERPINA3 C9 KIT BMP1 GHR AKR7A2 IGFBP4 CRP CNTN1 0.876

 17

 KLK3-SERPINA3 C9 KIT AHSG GHR BMPER IGFBP4 AKR7A2 CNTN1 0.876

 18

 KLK3-SERPINA3 C9 BMPER KIT IGFBP2 AKR7A2 GHR CRP CNTN1 0.876

 19

 KLK3-SERPINA3 AHSG KIT BMPER GHR AKR7A2 CNTN1 CRP C9 0.876

 twenty

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP CNTN1 ITIH4 BMP1 0.876

 twenty-one

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP GHR ITIH4 CNTN1 0.876

 22

 KLK3-SERPINA3 BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 BMP1 0.876

 2. 3

 KLK3-SERPINA3 CA6 KIT BMPER GHR AKR7A2 IGFBP4 ITIH4 CNTN1 0.876

 24

 KLK3-SERPINA3 BMPER IGFBP2 AKR7A2 IGFBP4 CRP CNTN1 ITIH4 BMP1 0.876

 25

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 CRP CNTN1 ITIH4 BMP1 0.876

 26

 KLK3-SERPINA3 BMP1 KIT AHSG IGFBP4 BMPER CNTN1 AKR7A2 C9 0.876

 27

 KLK3-SERPINA3 AHSG KIT BMPER IGFBP2 AKR7A2 GHR CRP CNTN1 0.876

 28

 KLK3-SERPINA3 BMPER IGFBP2 AKR7A2 GHR CRP IGFBP4 ITIH4 CNTN1 0.876

 29

 KLK3-SERPINA3 AHSG BMPER KIT IGFBP4 AKR7A2 CNTN1 ITIH4 C9 0.875

 30

 KLK3-SERPINA3 AHSG KIT BMPER GHR AKR7A2 CNTN1 CRP BMP1 0.875

 31

 KLK3-SERPINA3 BMP1 KIT AKR7A2 IGFBP2 CRP IGFBP4 ITIH4 CNTN1 0.875

 32

 KLK3-SERPINA3 C9 BMPER KIT IGFBP2 AKR7A2 IGFBP4 ITIH4 CNTN1 0.875

 33

 KLK3-SERPINA3 C9 KIT AKR7A2 GHR CRP IGFBP4 ITIH4 CNTN1 0.875

 3. 4

 KLK3-SERPINA3 BMP1 BMPER KIT GHR NME2 IGFBP4 CRP CNTN1 0.875

 35

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP IGFBP4 ITIH4 CA6 0.875

 36

 KLK3-SERPINA3 C9 KIT CA6 GHR BMPER IGFBP4 AKR7A2 CNTN1 0.875

 37

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP GHR ITIH4 IGFBP4 0.875

 38

 KLK3-SERPINA3 AHSG KIT BMPER GHR AKR7A2 IGFBP4 ITIH4 C9 0.875

 39

 KLK3-SERPINA3 BMP1 KIT BMPER GHR AKR7A2 CNTN1 ITIH4 C9 0.875

 40

 KLK3-SERPINA3 CA6 KIT BMPER GHR AKR7A2 CNTN1 CRP C9 0.875

 41

 KLK3-SERPINA3 BMPER KIT NME2 IGFBP4 CRP CNTN1 ITIH4 BMP1 0.875

 42

 KLK3-SERPINA3 BMP1 BMPER KIT IGFBP2 AKR7A2 IGFBP4 ITIH4 CNTN1 0.875

 43

 KLK3-SERPINA3 AHSG KIT BMPER IGFBP4 AKR7A2 CNTN1 ITIH4 BMP1 0.875

 44

 KLK3-SERPINA3 BMP1 BMPER KIT IGFBP2 AKR7A2 IGFBP4 CRP CNTN1 0.875

 Four. Five

 KLK3-SERPINA3 BMP1 KIT AKR7A2 GHR CRP IGFBP4 ITIH4 C9 0.875

 46

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP IGFBP4 ITIH4 AHSG 0.874

 47

 KLK3-SERPINA3 DDC KIT BMPER IGFBP4 AKR7A2 CNTN1 ITIH4 BMP1 0.874

 48

 KLK3-SERPINA3 DDC BMPER KIT IGFBP2 AKR7A2 CNTN1 ITIH4 BMP1 0.874

 49

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 CRP IGFBP4 ITIH4 C9 0.874

 fifty

 KLK3-SERPINA3 C9 KIT AHSG EGFR BMPER GHR AKR7A2 CNTN1 0.874

 51

 KLK3-SERPINA3 CNTN1 BMPER KIT IGFBP2 AKR7A2 GHR CRP IGFBP4 0.874

 52

 KLK3-SERPINA3 BMP1 BMPER KIT GHR AKR7A2 IGFBP4 ITIH4 C9 0.874

 53

 KLK3-SERPINA3 C9 KIT SERPINA1 GHR BMPER IGFBP4 AKR7A2 CNTN1 0.874

 54

 KLK3-SERPINA3 IGFBP2 IGFBP4 CNTN1 AHSG 0.874

 BMPER AKR7A2 CRP ITIH4

 55

 KLK3-SERPINA3 KIT IGFBP2 CNTN1 C9 0.874

 DDC BMPER AKR7A2 ITIH4

 56

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.874

 C9 AHSG BMPER AKR7A2

 57

 KLK3-SERPINA3 KIT IGFBP2 CNTN1 C9 0.874

 BMP1 BMPER AKR7A2 CRP

 58

 KLK3-SERPINA3 KIT IGFBP2 CNTN1 C9 0.874

 AHSG BMPER AKR7A2 CRP

 59

 KLK3-SERPINA3 KIT GHR CNTN1 BMP1 0.874

 FN1 BMPER AKR7A2 CRP

 60

 KLK3-SERPINA3 GHR IGFBP4 CNTN1 C9 0.874

 BMPER AKR7A2 CRP ITIH4

 61

 KLK3-SERPINA3 KIT IGFBP2 CNTN1 C9 0.874

 BMP1 AHSG BMPER AKR7A2

 62

 KLK3-SERPINA3 KIT IGFBP2 GHR IGFBP4 0.874

 CNTN1 C9 BMPER AKR7A2

 63

 GHR IGFBP4 CNTN1 C9 0.874 KIT

 BMPER AKR7A2 CRP ITIH4

 64

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.874

 CA6 BMPER AKR7A2 CRP

 65

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.874

 CA6 AKR7A2 CRP ITIH4

 66

 KLK3-SERPINA3 KIT GHR CNTN1 C9 0.874

 BMP1 FN1 BMPER AKR7A2

 67

 KLK3-SERPINA3 KIT GHR IGFBP4 FN1 0.874

 BMPER AKR7A2 CRP ITIH4

 68

 GHR KIT IGFBP4 C9 BMP1 0.874

 BMPER AKR7A2 CRP ITIH4

 69

 KLK3-SERPINA3 KIT GHR CNTN1 CA6 0.874

 BMPER AKR7A2 CRP ITIH4

 70

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.874

 BMP1 NME2 CRP ITIH4

 71

 KLK3-SERPINA3 KIT IGFBP2 IGFBP4 BMP1 0.874

 BMPER AKR7A2 CRP ITIH4

 72

 KLK3-SERPINA3 BDNF KIT GHR CNTN1 0.874

 C9 BMPER AKR7A2 CRP

 73

 KLK3-SERPINA3 KIT GHR IGFBP4 C9 0.874

 AHSG BMPER AKR7A2 CRP

 74

 KLK3-SERPINA3 KIT IGFBP4 CNTN1 C9 0.874

 BMP1 AKR7A2 CRP ITIH4

 75

 KLK3-SERPINA3 CA6 KIT BMPER GHR AKR7A2 IGFBP4 ITIH4 C9 0.874

 76

 KLK3-SERPINA3 CA6 KIT BMPER GHR AKR7A2 CNTN1 ITIH4 C9 0.874

 77

 KIT BMP1 IGFBP2 BMPER GHR AKR7A2 CNTN1 CRP C9 0.874

 78

 KLK3-SERPINA3 C9 BDNF BMPER KIT AKR7A2 IGFBP2 CRP CNTN1 0.874

 79

 KLK3-SERPINA3 C9 KIT AHSG IGFBP2 BMPER EGFR AKR7A2 CNTN1 0.874

 80

 KLK3-SERPINA3 BMPER KIT NME2 GHR CRP IGFBP4 ITIH4 CNTN1 0.874

 81

 KLK3-SERPINA3 BMP1 KIT AHSG EGFR BMPER CNTN1 AKR7A2 C9 0.874

 82

 KLK3-SERPINA3 BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 CA6 0.874

 83

 KLK3-SERPINA3 BMPER KIT NME2 GHR CRP IGFBP4 ITIH4 BMP1 0.874

 84

 KLK3-SERPINA3 AKR7A2 KIT NME2 IGFBP4 CRP CNTN1 ITIH4 BMP1 0.874

 85

 KLK3-SERPINA3 DDC BMPER KIT IGFBP4 AKR7A2 CNTN1 ITIH4 C9 0.874

 86

 KIT AHSG IGFBP2 BMPER GHR AKR7A2 CNTN1 CRP C9 0.874

 87

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR CRP CNTN1 ITIH4 C9 0.874

 88

 KLK3-SERPINA3 BMP1 KIT DDC GHR BMPER CNTN1 AKR7A2 C9 0.874

 89

 KLK3-SERPINA3 AHSG KIT BMPER GHR AKR7A2 C9 CRP BMP1 0.874

 90

 KLK3-SERPINA3 CA6 KIT BMPER GHR AKR7A2 CNTN1 CRP BMP1 0.874

 91

 KLK3-SERPINA3 DDC BMPER KIT IGFBP2 AKR7A2 CNTN1 CRP BMP1 0.874

 92

 KIT AHSG GHR BMPER CNTN1 AKR7A2 C9 CRP BMP1 0.874

 93

 KLK3-SERPINA3 AHSG BMPER KIT IGFBP2 AKR7A2 CNTN1 CRP BMP1 0.874

 94

 KLK3-SERPINA3 DDC KIT AKR7A2 IGFBP2 CRP CNTN1 ITIH4 BMP1 0.874

 95

 KLK3-SERPINA3 BMPER IGFBP2 AKR7A2 IGFBP4 CRP CNTN1 ITIH4 DDC 0.874

 96

 KLK3-SERPINA3 BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 AHSG 0.874

 97

 KLK3-SERPINA3 CA6 BMPER KIT IGFBP2 AKR7A2 GHR CRP CNTN1 0.874

 98

 KLK3-SERPINA3 BMP1 KIT SERPINA1 GHR BMPER IGFBP4 AKR7A2 CNTN1 0.874

 99

 KIT BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 BMP1 0.874

 100

 KLK3-SERPINA3 BMP1 KIT AHSG GHR AKR7A2 CNTN1 CRP C9 0.874

Table 29: 10 biomarker panels

 Markers

 Average VC ABC

 one

 KLK3-SERPINA3 CNTN1 BMPER KIT IGFBP2 AKR7A2 GHR CRP IGFBP4 ITIH4 0.880

 2

 KLK3-SERPINA3 BMP1 BMPER GHR KIT AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.880

 3

 KLK3-SERPINA3 CA6 KIT BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.878

 4

 KLK3-SERPINA3 BMP1 BMPER KIT IGFBP2 AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.878

 5

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 NME2 CNTN1 CRP C9 ITIH4 0.878

 6

 KLK3-SERPINA3 C9 KIT BMP1 GHR BMPER IGFBP4 AKR7A2 CNTN1 ITIH4 0.878

 7

 KLK3-SERPINA3 BMP1 BMPER GHR KIT AKR7A2 IGFBP4 CRP C9 ITIH4 0.878

 8

 KLK3-SERPINA3 C9 BMPER KIT GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.877

 9

 KLK3-SERPINA3 BMP1 KIT AHSG GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.877

 10

 KLK3-SERPINA3 BMP1 BMPER KIT GHR NME2 IGFBP4 CRP CNTN1 ITIH4 0.877

 eleven

 KLK3-SERPINA3 C9 KIT AHSG IGFBP2 BMPER GHR AKR7A2 CNTN1 CRP 0.877

 12

 KLK3-SERPINA3 C9 KIT CA6 GHR BMPER IGFBP4 AKR7A2 CNTN1 ITIH4 0.877

 13

 KLK3-SERPINA3 AHSG BMPER KIT IGFBP2 AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.877

 14

 KLK3-SERPINA3 BMP1 KIT CA6 GHR BMPER IGFBP4 AKR7A2 CNTN1 ITIH4 0.877

 fifteen

 KLK3-SERPINA3 C9 KIT BMP1 GHR BMPER IGFBP4 AKR7A2 CNTN1 CRP 0.877

 16

 KLK3-SERPINA3 BMP1 KIT CA6 GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.877

 17

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP4 NME2 CNTN1 CRP BMP1 ITIH4 0.877

 18

 KLK3-SERPINA3 BMP1 KIT AHSG IGFBP4 BMPER CNTN1 AKR7A2 C9 ITIH4 0.877

 19

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.877

 C9 BMP1 AHSG BMPER AKR7A2

 twenty

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.876

 AHSG BMPER AKR7A2 CRP ITIH4

 twenty-one

 KLK3-SERPINA3 KIT GHR IGFBP4 C9 0.876

 AHSG BMPER AKR7A2 CRP ITIH4

 22

 KLK3-SERPINA3 KIT GHR CNTN1 C9 0.876

 BMP1 AHSG BMPER AKR7A2 CRP

 2. 3

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.876

 C9 BMP1 AKR7A2 CRP ITIH4

 24

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.876

 C9 AHSG BMPER AKR7A2 ITIH4

 25

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.876

 BMP1 AHSG BMPER AKR7A2 CRP

 26

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.876

 C9 AHSG BMPER AKR7A2 CRP

 27

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.876

 BMP1 BMPER AKR7A2 CRP ITIH4

 28

 KLK3-SERPINA3 KIT IGFBP4 CNTN1 C9 0.876

 BMP1 BMPER AKR7A2 NME2 ITIH4

 29

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.876

 CA6 BMPER AKR7A2 CRP ITIH4

 30

 KLK3-SERPINA3 GHR IGFBP4 CNTN1 BMP1 0.876

 AHSG BMPER AKR7A2 CRP ITIH4

 31

 KLK3-SERPINA3 KIT IGFBP2 GHR IGFBP4 0.876

 CNTN1 BMP1 BMPER AKR7A2 CRP

 32

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.876

 C9 BMP1 BMPER AKR7A2 CRP

 33

 KLK3-SERPINA3 KIT GHR IGFBP4 C9 0.876

 BMPER AKR7A2 NME2 CRP ITIH4

 3. 4

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.876

 C9 CA6 BMPER AKR7A2 CRP

 35

 KLK3-SERPINA3 KIT GHR CNTN1 C9 0.876

 CA6 AHSG BMPER AKR7A2 CRP

 36

 KLK3-SERPINA3 KIT IGFBP2 CNTN1 BMP1 0.876

 AHSG BMPER AKR7A2 CRP ITIH4

 37

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.876

 C9 CA6 AHSG BMPER AKR7A2

 38

 KLK3-SERPINA3 KIT GHR CNTN1 C9 0.876

 BMP1 FN1 BMPER AKR7A2 CRP

 39

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.875

 BMP1 CA6 BMPER AKR7A2 CRP

 40

 KLK3-SERPINA3 C9 KIT BMP1 GHR AHSG IGFBP4 AKR7A2 CNTN1 CRP 0.875

 41

 KLK3-SERPINA3 IGFBP2 GHR IGFBP4 CNTN1 0.875

 C9 BMPER AKR7A2 CRP ITIH4

 42

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.875

 BMP1 AHSG BMPER AKR7A2 CRP

 43

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.875

 C9 DDC BMPER AKR7A2 ITIH4

 44

 KLK3-SERPINA3 KIT IGFBP2 GHR IGFBP4 0.875

 CNTN1 C9 BMPER AKR7A2 CRP

 Four. Five

 KLK3-SERPINA3 KIT IGFBP2 CNTN1 BMP1 0.875

 DDC BMPER AKR7A2 CRP ITIH4

 46

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.875

 AHSG BMPER AKR7A2 CRP ITIH4

 47

 KLK3-SERPINA3 KIT IGFBP4 CNTN1 C9 0.875

 BMP1 AKR7A2 NME2 CRP ITIH4

 48

 KLK3-SERPINA3 KIT IGFBP2 IGFBP4 CNTN1 0.875

 BMP1 DDC AKR7A2 CRP ITIH4

 49

 KLK3-SERPINA3 GHR IGFBP4 CNTN1 C9 0.875

 AHSG BMPER AKR7A2 CRP ITIH4

 fifty

 KLK3-SERPINA3 KIT IGFBP2 GHR IGFBP4 0.875

 C9 BMPER AKR7A2 CRP ITIH4

 51

 KLK3-SERPINA3 KIT GHR CNTN1 C9 0.875

 BMPER AKR7A2 NME2 CRP ITIH4

 52

 KLK3-SERPINA3 KIT IGFBP4 CNTN1 BMP1 0.875

 CA6 BMPER AKR7A2 CRP ITIH4

 53

 KLK3-SERPINA3 GHR IGFBP4 BMP1 0.875 KIT

 CA6 BMPER AKR7A2 CRP ITIH4

 54

 KLK3-SERPINA3 KIT IGFBP2 GHR IGFBP4 0.875

 CNTN1 C9 BMPER AKR7A2 ITIH4

 55

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.875

 BMPER AKR7A2 NME2 CRP ITIH4

 56

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.875

 C9 CA6 BMPER AKR7A2 CRP

 57

 KLK3-SERPINA3 KIT GHR CNTN1 BMP1 0.875

 AHSG BMPER AKR7A2 CRP ITIH4

 58

 KLK3-SERPINA3 KIT IGFBP2 CNTN1 C9 0.875

 BMPER AKR7A2 NME2 CRP ITIH4

 59

 KLK3-SERPINA3 KIT GHR IGFBP4 C9 0.875

 FN1 BMPER AKR7A2 CRP ITIH4

 60

 KLK3-SERPINA3 KIT IGFBP2 IGFBP4 CNTN1 0.875

 C9 AHSG BMPER AKR7A2 ITIH4

 61

 KLK3-SERPINA3 KIT GHR CNTN1 C9 0.875

 BMP1 FN1 AHSG BMPER AKR7A2

 62

 KLK3-SERPINA3 BMP1 IGFBP2 BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.875

 63

 KLK3-SERPINA3 BMP1 KIT DDC GHR AHSG CNTN1 BMPER C9 AKR7A2 0.875

 64

 KLK3-SERPINA3 FN1 BMPER GHR KIT AKR7A2 IGFBP4 CRP BMP1 ITIH4 0.875

 65

 KLK3-SERPINA3 BMPER KIT AKR7A2 GHR NME2 IGFBP4 CRP BMP1 ITIH4 0.875

 66

 KLK3-SERPINA3 DDC BMPER KIT IGFBP2 AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.875

 67

 KLK3-SERPINA3 BMP1 BMPER KIT EGFR AKR7A2 GHR CRP CNTN1 ITIH4 0.875

 68

 KLK3-SERPINA3 BMP1 KIT AHSG GHR BMPER IGFBP4 AKR7A2 CNTN1 ITIH4 0.875

 69

 KLK3-SERPINA3 C9 KIT BMP1 GHR BMPER IGFBP4 AKR7A2 CNTN1 NME2 0.875

 70

 KLK3-SERPINA3 C9 BMPER KIT GHR AKR7A2 IGFBP4 NME2 CNTN1 CRP 0.875

 71

 KLK3-SERPINA3 CA6 GHR BMPER IGFBP4 AKR7A2 CNTN1 CRP BMP1 ITIH4 0.875

 72

 KLK3-SERPINA3 C9 KIT SERPINA1 GHR AHSG IGFBP4 BMPER CNTN1 AKR7A2 0.875

 73

 KLK3-SERPINA3 BMP1 GHR BMPER IGFBP4 AKR7A2 CNTN1 CRP C9 ITIH4 0.875

 74

 KLK3-SERPINA3 AHSG KIT BMPER GHR AKR7A2 IGFBP4 CRP BMP1 ITIH4 0.875

 75

 KLK3-SERPINA3 C9 BMPER KIT GHR AKR7A2 IGFBP4 NME2 CNTN1 ITIH4 0.875

 76

 KLK3-SERPINA3 BMP1 BMPER GHR KIT AKR7A2 CNTN1 NME2 C9 CRP 0.875

 77

 KLK3-SERPINA3 C9 BMPER KIT IGFBP2 AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.875

 78

 KLK3-SERPINA3 DDC BMPER KIT IGFBP2 AKR7A2 GHR CRP CNTN1 ITIH4 0.875

 79

 KLK3-SERPINA3 BMPER KIT AKR7A2 IGFBP2 NME2 IGFBP4 CRP CNTN1 ITIH4 0.875

 80

 KLK3-SERPINA3 CNTN1 KIT CA6 IGFBP2 BMPER GHR AKR7A2 IGFBP4 CRP 0.875

 81

 KLK3-SERPINA3 AHSG IGFBP2 BMPER GHR AKR7A2 IGFBP4 CRP CNTN1 ITIH4 0.875

 82

 KLK3-SERPINA3 C9 KIT BMP1 GHR SERPINA1 IGFBP4 BMPER CNTN1 AKR7A2 0.875

 83

 KLK3-SERPINA3 BMP1 KIT DDC IGFBP4 BMPER CNTN1 AKR7A2 C9 ITIH4 0.875

 84

 KLK3-SERPINA3 BMP1 KIT AHSG GHR BMPER IGFBP4 AKR7A2 C9 CRP 0.875

 85

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.875

 BMP1 FN1 BMPER AKR7A2 CRP

 86

 KLK3-SERPINA3 KIT GHR CNTN1 BMP1 0.875

 FN1 AHSG BMPER AKR7A2 CRP

 87

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.875

 C9 FN1 BMPER AKR7A2 CRP

 88

 KLK3-SERPINA3 KIT IGFBP2 IGFBP4 CNTN1 0.875

 C9 BMP1 BMPER AKR7A2 ITIH4

 89

 KLK3-SERPINA3 KIT GHR IGFBP4 C9 0.875

 CA6 BMPER AKR7A2 CRP ITIH4

 90

 KLK3-SERPINA3 KIT IGFBP2 IGFBP4 C9 0.875

 AHSG BMPER AKR7A2 CRP ITIH4

 91

 GHR IGFBP4 CNTN1 C9 0.875 KIT

 BMPER AKR7A2 NME2 CRP ITIH4

 92

 KLK3-SERPINA3 KIT GHR IGFBP4 C9 0.875

 BMP1 AHSG AKR7A2 CRP ITIH4

 93

 KLK3-SERPINA3 KIT IGFBP2 IGFBP4 CNTN1 0.875

 BMP1 AHSG BMPER AKR7A2 CRP

 94

 KLK3-SERPINA3 KIT GHR IGFBP4 C9 0.875

 BMP1 FN1 AKR7A2 CRP ITIH4

 95

 KLK3-SERPINA3 KIT IGFBP2 IGFBP4 C9 0.875

 BMPER AKR7A2 NME2 CRP ITIH4

 96

 KLK3-SERPINA3 KIT IGFBP4 C9 BMP1 0.875

 BMPER AKR7A2 NME2 CRP ITIH4

 97

 KLK3-SERPINA3 KIT GHR IGFBP4 CNTN1 0.875

 DDC BMPER AKR7A2 CRP ITIH4

 98

 KLK3-SERPINA3 KIT IGFBP2 GHR CNTN1 0.875

 C9 FN1 BMPER AKR7A2 CRP

 99

 KLK3-SERPINA3 KIT IGFBP2 GHR IGFBP4 0.875

 BMP1 BMPER AKR7A2 CRP ITIH4

 100

 KLK3-SERPINA3 KIT GHR CNTN1 BMP1 0.874

 DDC BMPER AKR7A2 CRP ITIH4

Table 30: Marker counts on biomarker panels

 Panel size

 Biomarker

 3 4 5 6 7 8 9 10

 AHSG

 118 104 104 117 135 211 284 376

 AKR7A2

 205 485 676 738 810 859 921 950

 BDNF

 143 212 185 171 162 125 113 78

 BMP1

 127 157 214 273 308 404 457 495

 BMPER

 168 205 346 471 572 673 750 820

 C9

 197 313 402 466 515 536 543 587

 CA6

 107 96 88 74 96 120 165 223

 CKB-CKM

 40 1 0 0 0 0 0 0

 CNTN1

 137 164 235 420 579 717 763 815

 CRP

 183 267 407 506 558 588 671 721

 DDC

 110 93 93 109 129 154 161 197

 EGFR

 135 162 190 196 193 170 177 179

 FGA-FGB-FGG

 34 0 0 0 0 0 0 0

 FN1

 90 46 13 11 18 44 70 103

 GHR

 107 98 126 181 261 398 513 611

 IGFBP2

 123 127 176 211 277 320 360 380

 IGFBP4

 97 112 152 198 265 356 461 570

 ITIH4

 143 148 214 272 379 455 542 636

 KIT

 147 201 290 481 626 760 836 881

 KLK3-SERPINA3

 213 448 592 721 809 851 916 947

 NME2

 177 337 365 307 245 198 215 310

 SERPINA1

 83 91 56 31 25 35 60 104

 STX1A

 116 133 76 46 38 26 22 17

Table 31: Parameters obtained from established cancer data sets for classifiers _______________________________ simplebayesians______________________________

 CPNM mesothelioma Renal cell Carc.

 Cancer Control Cancer Control Cancer Control

 AKR7A2

 Mean 6.65 7.35 6.76 7.16 7.48 7.16

 DT 0.51 0.48 0.43 0.25 0.58 0.39

 BMPER

 Mean 7.31 7.06 7.45 7.32 7.33 7.21

 DT 0.21 0.25 0.11 0.16 0.11 0.20

 CNTN1

 Average 9.15 8.89 9.26 9.15 9.14 8.90

 DT 0.21 0.36 0.18 0.11 0.19 0.26

 CRP

 Average 7.84 9.79 7.73 9.00 8.32 10.59

 DT 1.06 1.96 1.09 1.42 1.63 1.39

 GHR

 Average 7.60 7.45 7.72 7.59 7.80 7.67

 DT 0.13 0.17 0.14 0.10 0.14 0.17

 IGFBP2

 Average 8.45 8.98 8.51 9.01 8.51 8.92

 DT 0.47 0.61 0.42 0.45 0.45 0.45

 IGFBP4

 Mean 7.89 8.05 8.14 8.27 8.15 8.36

 DT 0.15 0.24 0.14 0.16 0.20 0.22

 ITIH4

 Mean 10.18 10.46 10.60 10.74 10.56 10.82

 DT 0.32 0.34 0.12 0.23 0.15 0.20

 KIT

 Average 9.39 9.18 9.60 9.50 9.39 9.25

 DT 0.16 0.20 0.14 0.14 0.16 0.19

 KLK3-SERPINA3

 Average 8.00 8.51 8.10 8.33 8.09 8.68

 DT 0.16 0.53 0.19 0.33 0.23 0.48

Table 32: Calculations obtained from the training set for simple Bayesian classifier.

 Biomarker

 V Vd O0 Od X P (c \ x) P (dlx) ln (p (d \ x) / p (c \ x))

 BMPER

 7,450 7,323 0.108 0.164 7.045 0.003 0.576 5.176

 KIT

 9,603 9,503 0,139 0,141 9,534 2,546 2,767 0,083

 AKR7A2

 6,761 7,155 0.432 0.248 6.347 0.583 0.008 -4.309

 IGFBP4

 8,138 8,268 0,140 0,163 8,336 1,046 2,251 0,767

 GHR

 7,724 7,595 0,135 0.102 7,756 2,867 1,126 -0,935

 ITIH4

 10,596 10,738 0,121 0,227 10,600 3,301 1,460 -0,816

 IGFBP2

 8,514 9,006 0.417 0.448 8.812 0.741 0.811 0.091

 KLK3-SERPINA3

 8,102 8,327 0,194 0,330 7,909 1,253 0,542 -0,838

 CNTN1

 9,265 9,149 0,181 0,114 9,410 1,602 0,252 -1,848

 CRP

 7,733 9,005 1,095 1,422 7,675 0,364 0,181 -0,697

Claims (18)

5 10 fifteen twenty 25 30 35 40 Four. Five fifty 55 60 65 1. A method to diagnose that an individual has cancer or not, including the method: detecting, in a sample of whole blood, plasma or serum of an individual, a biomarker value corresponding to CA6, wherein said individual is classified as suffering or not suffering from cancer based on said biomarker value. 2. The method of claim 1 wherein the method further comprises detecting biomarker values corresponding to at least N biomarkers selected from the group of biomarkers set forth in Table 19 and wherein N = 2-23. 3. The method of claim 1 or claim 2, wherein detecting the biomarker values comprises performing an in vitro assay, wherein said in vitro assay preferably comprises at least one capture reagent corresponding to each of said biomarkers and which further comprising selecting said at least one capture reagent from the group consisting of aptamers and antibodies, preferably wherein said at least one capture reagent is an aptamer. 4. The method of claim 3, wherein the in vitro assay is selected from the group consisting of an immunoassay or an aptamer-based assay. 5. The method of any one of the preceding claims, wherein each biomarker value is evaluated based on a predetermined value or a predetermined range of values. 6. The method of any one of the preceding claims, wherein the individual is a human being. 7. The method of any of claims 2 to 6, wherein N = 2-15, N = 3-15, N = 3-10, N = 4-20, N = 4-15, N = 4-10 , N = 5-20, N = 5-15 or N = 5-10. 8. A method implemented by computer to indicate a probability of cancer, the method comprising: retrieve in a computer biomarker information for an individual, where the biomarker information comprises a biomarker value corresponding to CA6, wherein said biomarker was detected in a sample of whole blood, plasma or serum; perform a classification of the biomarker value with the computer; and indicate a probability that said individual has cancer based on the classification. 9. The method implemented by computer according to claim 8 wherein the biomarker information further comprises biomarker values that each correspond to one of at least N biomarkers selected from Table 19, wherein N = 3-12. 10. A computer program product to indicate a cancer probability, the computer program product comprising: a computer readable medium that incorporates program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data attributed to a biological sample of an individual, where the data comprises biomarker values corresponding to CA6, wherein said biomarker was detected in the whole blood, plasma or serum sample; Y code that executes a classification method that indicates an individual's cancer status based on said biomarker value. 11. The computer program product of claim 10 wherein the data further comprises biomarker values each corresponding to at least one biomarker and wherein N = 3-12. 12. The computer program product of claim 10 or claim 11, wherein said classification method uses a probability density function or wherein said classification method uses two or more classes. 13. The method according to any one of claims 1-7, wherein said individual is classified as suffering or not suffering from cancer based on said biomarker values and at least one additional biomedical information element corresponding to said individual. 14. The method according to claim 1 wherein the cancer is non-small cell lung cancer (NSCLC). 5 10 fifteen twenty 25 30 35 40 Four. Five fifty 55 60 65 15. The method according to claim 14 wherein the method further comprises detecting biomarker values corresponding to at least one of at least N biomarkers selected from the group of biomarkers set forth in Table 1 and wherein N = 2-59. 16. The method according to claim 13, wherein said at least one additional biomedical information element is independently selected from the group consisting of: (a) information corresponding to physical descriptors of said individual, (b) information corresponding to the radiological descriptors of a pulmonary anomaly in said individual, (c) information corresponding to the presence or absence of a pulmonary nodule in said individual, (d) information corresponding to the physical descriptors of a pulmonary nodule in said individual, (e) information corresponding to a change in the height and / or weight of said individual, (f) information corresponding to the ethnicity of said individual, (g) information corresponding to the gender of said individual, (h) information corresponding to the smoking history of said individual, (i) information corresponding to exposure to environmental tobacco in said individual, (j) information corresponding to the history of alcohol use in said individual, (k) information corresponding to the occupational history of said individual, (l) information corresponding to the family history of lung cancer or other cancer in said individual, (b) information corresponding to the presence or absence in said individual of at least one genetic marker correlated with an increased risk of lung cancer or cancer in said individual or a family member of said individual, (n) information corresponding to clinical symptoms of said individual, (o) information corresponding to other laboratory tests, (p) information corresponding to gene expression values of said individual and (q) information corresponding to the exposure of said individuals to known carcinogens. 17. A method according to any one of claims 1 to 7 or 13 to 16 comprising detecting biomarker values for the biomarkers: to. CA6; b. CA6, YWHAG; C. MMP7, CA6 and YWHAG; d. C9, CA6 and YWHAG; and. CA6, STX1A and YWHAG; F. KLK3-SERPINA3 and CA6; g. C9 and CA6; h. CA6 and CRP; i. CA6 and BMPER; j. CA6 and AKR7A2; k. KLK3-SERPINA3, CA6 and AKR7A2; l. KLK3-SERPINA3, CA6 and NME2; m. KLK3-SERPINA3, BDNF and CA6; n. KLK3-SERPINA3, C9, CA6 and AKR7A2; or. KLK3-SERPINA3, BMPER, KIT, AKR7A2, CNTN1, C9 and CA6; p. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, CNTN1 and C9; q. KLK3-SERPINA3, BMPER, KIT, AKR7A2, GHR, CRP, CNTN1 and CA6; r. KLK3-SERPINA3, BMPER, KIT, AKR7A2, GHR, ITIH4, CNTN1 and CA6; s. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, IGFBP4, ITIH4 and CNTN1; t. KLK3-SERPINA3, BMPER, KIT, AKR7A2, GHR, CRP, IGFBP4, ITIH4 and CA6; or. KLK3-SERPINA3, C9, KIT, CA6, GHR, BMPER, IGFBP4, AKR7A2 and CNTN1; v. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, CNTN1, CRP and C9; w. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, IGFBP4, CRP and CNTN1; x. KLK3-SERPINA3, CA6, KIT, AKR7A2, GHR, CRP, IGFBP4, ITIH4 and CNTN1; Y. KLK3-SERPINA3, BMPER, KIT, AKR7A2, GHR, CRP, CNTN1, ITIH4 and CA6; z. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, IGFBP4, ITIH4 and C9; aa. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, CNTN1, ITIH4 and C9; bb. KLK3-SERPINA3, BMPER, GHR, AKR7A2, IGFBP4, CRP, GNTN1, ITIH4 and CA6; DC. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, CNTN1, CRP and BMP1; dd. KLK3-SERPINA3, CA6, KIT, BMPER, IGFBP2, AKR7A2, GHR, CRP and CNTN1; usa KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, IGFBP4, CRP, CNTN1 and ITIH4; ff. KLK3-SERPINA3, C9, KIT, CA6, GHR, BMPER, IGFBP4, AKR7A2, CNTN1 and ITIH4; gg. KLK3-SERPINA3, BMP1, KIT, CA6, GHR, BMPER, IGFBP4, AKR7A2, CNTN1 and ITIH4; H H. KLK3-SERPINA3, BMP1, KIT, CA6, GHR, AKR7A2, IGFBP4, CRP, CNTN1 and ITIH4; ii. KLK3-SERPINA3, CA6, KIT, BMPER, IGFBP2, AKR7A2, GHR, CRP, CNTN1 and ITIH4; jj. KLK3-SERPINA3, C9, KIT, CA6, GHR, BMPER, IGRBP4, AKR7A2, CNTN1 and CRP; kk KLK3-SERPINA3, CA6, KIT, AHSG, GHR, BMPER, CNTN1, AKR7A2, C9 and CRP; ll. KLK3-SERPINA3, C9, KIT, CA6, GHR, AHSG, IGFBP4, BMPER, CNTN1 and AKR7A2; mm KLK3-SERPINA3, BMP1, KIT, CA6, GHR, BMPER, IGFBP4, AKR7A2, CNTN1 and CRP; nn. KLK3-SERPINA3, CA6, KIT, BMPER, IGFBP4, AKR7A2, CNTN1, CRP, BMP1 and ITIH4; 5 oo. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, IGFBP4, CRP, BMP1 and ITIH4; pp. KLK3-SERPINA3, C9, KIT, CA6, IGFBP2, BMPER, GHR, AKR7A2, CNTN1 and CRP; qq. KLK3-SERPINA3, CA6, GHR, BMPER, IGFBP4, AKR7A2, CNTN1, CRP, BMP1 and ITIH4; rr. KLK3-SERPINA3, CNTN1, KIT, CA6, IGFBP2, BMPER, GHR, AKR7A2, IGFBP4 and CRP; H.H. KLK3-SERPINA3, CA6, KIT, BMPER, GHR, AKR7A2, IGFBP4, CRP, C9 and ITIH4; 10 18. The method according to claim 1 wherein the cancer is lung cancer, pleural mesothelioma or renal cell carcinoma.